Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
 tags:
 - ultralyticsplus
-- yolov5
+- yolov8
 - ultralytics
 - yolo
 - computer-vision
@@ -27,16 +27,16 @@ metrics:
 pipeline_tag: object-detection
 ---
 
-# Model Card for YOLOv5 object detection
+# Model Card for YOLOv8 object detection
 
 ## Model Summary
 
-The YOLOv5  object Detection model is an object detection model based on the YOLO (You Only Look Once) framework. It is designed to detect object, whether they are object detect, in images. The model has been fine-tuned on a vast dataset and achieved high accuracy in detecting tables and distinguishing between object detect ones.
+The YOLOv8  object Detection model is an object detection model based on the YOLO (You Only Look Once) framework. It is designed to detect object, whether they are object detect, in images. The model has been fine-tuned on a vast dataset and achieved high accuracy in detecting tables and distinguishing between object detect ones.
 
 ## Model Details
 
 ### Model Description
-The YOLOv5 Object Detection model serves as a versatile solution for precisely identifying object detect within images, whether they exhibit a object detect. Notably, this model's capabilities extend beyond mere detection – it plays a crucial role for object detection. By employing advanced techniques such as object detection.
+The YOLOv8 Object Detection model serves as a versatile solution for precisely identifying object detect within images, whether they exhibit a object detect. Notably, this model's capabilities extend beyond mere detection – it plays a crucial role for object detection. By employing advanced techniques such as object detection.
 
 
 We invite you to explore the potential of this model and its object detection capabilities. For those interested in harnessing its power or seeking further collaboration, we encourage you to reach out to us at [email protected]. Whether you require assistance, customization, or have innovative ideas, our collaborative approach is geared towards addressing your unique challenges. Additionally, you can actively engage with our vibrant community section for valuable insights and collective problem-solving. Your input drives our continuous improvement, as we collectively pave the way towards enhanced object detection.
@@ -45,13 +45,13 @@ We invite you to explore the potential of this model and its object detection ca
 - **Model type:** Object Detection
 - **Task:** Object Detection (object detection)
 
-Furthermore, the YOLOv5  Detection model is limited to object detectionsetup environment python setup.py
-yolov5 object detection model alone. It is a versatile tool that contributes to the processing of structured image data. By utilizing advanced box techniques, the model empowers users to isolate object within the image visual data. What sets this model apart is its seamless integration with object detection technology. The combination of box information allows for precise object detection from the data. This comprehensive approach streamlines the process of information retrieval from image data.
+Furthermore, the YOLOv8  Detection model is limited to object detectionsetup environment python setup.py
+yolov8 object detection model alone. It is a versatile tool that contributes to the processing of structured image data. By utilizing advanced box techniques, the model empowers users to isolate object within the image visual data. What sets this model apart is its seamless integration with object detection technology. The combination of box information allows for precise object detection from the data. This comprehensive approach streamlines the process of information retrieval from image data.
 
 User collaboration is actively encouraged to enrich the model's capabilities. By contributing table images of different designs and types, users play a pivotal role in enhancing the model's ability to detect a diverse range of object accurately. Community participation can be facilitated through our platform or by reaching out to us at [email protected]. We value collaborative efforts that drive continuous improvement and innovation in object detection.
 
 ### Supported Labels
- YOLOv5
+ YOLOv8
 ```
 ['box', 'object detect']
 ```
@@ -60,7 +60,7 @@ User collaboration is actively encouraged to enrich the model's capabilities. By
 
 ### Direct Use
 
-The YOLOv5 and opencv for object Detection  model can be directly used for detecting object from images, whether they are bordered box.
+The YOLOv8 and opencv for object Detection  model can be directly used for detecting object from images, whether they are bordered box.
 
 ### Downstream Use
 
@@ -124,7 +124,7 @@ render.show()
 - Finetune the model on your custom dataset:
 
 ```bash
-yolov5 train --data dataset.yaml --img 640 --batch -1 --weights foduucom/object_detection--epochs 10
+yolov8 train --data dataset.yaml --img 640 --batch -1 --weights foduucom/object_detection--epochs 10
 ```
 
 
@@ -148,7 +148,7 @@ For inquiries and contributions, please contact us at [email protected].
 @ModelCard{
   author    = {Nehul Agrawal and
                Rahul parihar},
-  title     = {YOLOv5 object Detection},
+  title     = {YOLOv8 object Detection},
   year      = {2023}
 }
-``` YOLOv5
+```