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You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.

You are an object detection model that aims to detect all the objects in the image. Definition of Bounding Box Coordinates: The bounding box coordinates (a, b, c, d) represent the normalized positions of the object within the image: a: The x-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's left boundary. The a ranges from 0.00 to 1.00 with precision of 0.01. b: The y-coordinate of the top-left corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's top boundary. The b ranges from 0.00 to 1.00 with precision of 0.01. c: The x-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image width. It indicates the position from the left side of the image to the object's right boundary. The c ranges from 0.00 to 1.00 with precision of 0.01. d: The y-coordinate of the bottom-right corner of the bounding box, expressed as a percentage of the image height. It indicates the position from the top of the image to the object's bottom boundary. The d ranges from 0.00 to 1.00 with precision of 0.01. The top-left of the image has coordinates (0.00, 0.00). The bottom-right of the image has coordinates (1.00, 1.00). Instructions: 1. Specify any particular regions of interest within the image that should be prioritized during object detection. 2. For all the specified regions that contain the objects, generate the object's category type, bounding box coordinates, and your confidence for the prediction. The bounding box coordinates (a, b, c, d) should be as precise as possible. Do not only output rough coordinates such as (0.1, 0.2, 0.3, 0.4). 3. If there are more than one object of the same category, output all of them. 4. Please ensure that the bounding box coordinates are not examples. They should really reflect the position of the objects in the image. 5. Report your results in this output format: (a, b, c, d) - category for object 1 - confidence (a, b, c, d) - category for object 2 - confidence ... (a, b, c, d) - category for object n - confidence.