YOLO11 and RF-DETR-B added to YOLO-ARENA

README.md

@@ -1,13 +1,14 @@
 ---
 title: YOLO ARENA
 emoji: 🏟️
-colorFrom: pink
-colorTo: green
+colorFrom: yellow
+colorTo: pink
 sdk: gradio
-sdk_version: 4.19.2
+sdk_version: 5.22.0
 app_file: app.py
 pinned: false
 license: mit
+short_description: 'compare performance of top object detectors'
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -8,61 +8,25 @@ from inference import get_model
 MARKDOWN = """
 <h1 style='text-align: center'>YOLO-ARENA 🏟️</h1>
 
-Welcome to YOLO-Arena! This demo showcases the performance of various YOLO models 
-pre-trained on the COCO dataset.
-
-- **YOLOv8**
-  <div style="display: flex; align-items: center;">
-    <a href="https://github.com/ultralytics/ultralytics" style="margin-right: 10px;">
-      <img src="https://badges.aleen42.com/src/github.svg">
-    </a>
-    <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov8-object-detection-on-custom-dataset.ipynb" style="margin-right: 10px;">
-      <img src="https://colab.research.google.com/assets/colab-badge.svg">
-    </a>
-  </div>
-
-- **YOLOv9**
-  <div style="display: flex; align-items: center;">
-    <a href="https://github.com/WongKinYiu/yolov9" style="margin-right: 10px;">
-      <img src="https://badges.aleen42.com/src/github.svg">
-    </a>
-    <a href="https://arxiv.org/abs/2402.13616" style="margin-right: 10px;">
-      <img src="https://img.shields.io/badge/arXiv-2402.13616-b31b1b.svg">
-    </a>
-    <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov9-object-detection-on-custom-dataset.ipynb" style="margin-right: 10px;">
-      <img src="https://colab.research.google.com/assets/colab-badge.svg">
-    </a>
-  </div>
-
-- **YOLOv10**
-  <div style="display: flex; align-items: center;">
-    <a href="https://github.com/THU-MIG/yolov10" style="margin-right: 10px;">
-      <img src="https://badges.aleen42.com/src/github.svg">
-    </a>
-    <a href="https://arxiv.org/abs/2405.14458" style="margin-right: 10px;">
-      <img src="https://img.shields.io/badge/arXiv-2405.14458-b31b1b.svg">
-    </a>
-    <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb" style="margin-right: 10px;">
-      <img src="https://colab.research.google.com/assets/colab-badge.svg">
-    </a>
-  </div>
-
-Powered by Roboflow [Inference](https://github.com/roboflow/inference) and 
-[Supervision](https://github.com/roboflow/supervision). 🔥
+Welcome to YOLO-Arena! This demo showcases the performance of top object detection models pre-trained on the COCO dataset.
+
+Powered by [inference](https://github.com/roboflow/inference) and [supervision](https://github.com/roboflow/supervision). 💜
 """
 
 IMAGE_EXAMPLES = [
-    ['https://media.roboflow.com/supervision/image-examples/people-walking.png', 0.3, 0.3, 0.1],
-    ['https://media.roboflow.com/supervision/image-examples/vehicles.png', 0.3, 0.3, 0.1],
-    ['https://media.roboflow.com/supervision/image-examples/basketball-1.png', 0.3, 0.3, 0.1],
+    ['https://media.roboflow.com/supervision/image-examples/people-walking.png', 0.3, 0.3, 0.1, 0.3, 0.3],
+    ['https://media.roboflow.com/supervision/image-examples/vehicles.png', 0.3, 0.3, 0.1, 0.3, 0.4],
+    ['https://media.roboflow.com/supervision/image-examples/basketball-1.png', 0.3, 0.3, 0.1, 0.3, 0.4],
 ]
 
 YOLO_V8_MODEL = get_model(model_id="coco/8")
 YOLO_V9_MODEL = get_model(model_id="coco/17")
 YOLO_V10_MODEL = get_model(model_id="coco/22")
+YOLO11_MODEL = get_model(model_id="coco/27")
+RF_DETR_MODEL = get_model(model_id="coco/36")
 
-LABEL_ANNOTATORS = sv.LabelAnnotator(text_color=sv.Color.black())
-BOUNDING_BOX_ANNOTATORS = sv.BoundingBoxAnnotator()
+LABEL_ANNOTATORS = sv.LabelAnnotator(text_color=sv.Color.BLACK)
+BOUNDING_BOX_ANNOTATORS = sv.BoxAnnotator()
 
 
 def detect_and_annotate(
@@ -105,19 +69,27 @@ def process_image(
     yolo_v8_confidence_threshold: float,
     yolo_v9_confidence_threshold: float,
     yolo_v10_confidence_threshold: float,
+    yolo11_confidence_threshold: float,
+    rf_detr_confidence_threshold: float,
     iou_threshold: float
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     yolo_v8_annotated_image = detect_and_annotate(
         YOLO_V8_MODEL, input_image, yolo_v8_confidence_threshold, iou_threshold)
     yolo_v9_annotated_image = detect_and_annotate(
         YOLO_V9_MODEL, input_image, yolo_v9_confidence_threshold, iou_threshold)
     yolo_10_annotated_image = detect_and_annotate(
         YOLO_V10_MODEL, input_image, yolo_v10_confidence_threshold, iou_threshold)
+    yolo11_annotated_image = detect_and_annotate(
+        YOLO11_MODEL, input_image, yolo11_confidence_threshold, iou_threshold)
+    rf_detr_annotated_image = detect_and_annotate(
+        RF_DETR_MODEL, input_image, rf_detr_confidence_threshold, iou_threshold)
 
     return (
         yolo_v8_annotated_image,
         yolo_v9_annotated_image,
-        yolo_10_annotated_image
+        yolo_10_annotated_image,
+        yolo11_annotated_image,
+        rf_detr_annotated_image
     )
 
 
@@ -127,12 +99,7 @@ yolo_v8_confidence_threshold_component = gr.Slider(
     value=0.3,
     step=0.01,
     label="YOLOv8 Confidence Threshold",
-    info=(
-        "The confidence threshold for the YOLO model. Lower the threshold to "
-        "reduce false negatives, enhancing the model's sensitivity to detect "
-        "sought-after objects. Conversely, increase the threshold to minimize false "
-        "positives, preventing the model from identifying objects it shouldn't."
-    ))
+)
 
 yolo_v9_confidence_threshold_component = gr.Slider(
     minimum=0,
@@ -140,12 +107,7 @@ yolo_v9_confidence_threshold_component = gr.Slider(
     value=0.3,
     step=0.01,
     label="YOLOv9 Confidence Threshold",
-    info=(
-        "The confidence threshold for the YOLO model. Lower the threshold to "
-        "reduce false negatives, enhancing the model's sensitivity to detect "
-        "sought-after objects. Conversely, increase the threshold to minimize false "
-        "positives, preventing the model from identifying objects it shouldn't."
-    ))
+)
 
 yolo_v10_confidence_threshold_component = gr.Slider(
     minimum=0,
@@ -153,12 +115,22 @@ yolo_v10_confidence_threshold_component = gr.Slider(
     value=0.3,
     step=0.01,
     label="YOLOv10 Confidence Threshold",
-    info=(
-        "The confidence threshold for the YOLO model. Lower the threshold to "
-        "reduce false negatives, enhancing the model's sensitivity to detect "
-        "sought-after objects. Conversely, increase the threshold to minimize false "
-        "positives, preventing the model from identifying objects it shouldn't."
-    ))
+)
+
+yolo11_confidence_threshold_component = gr.Slider(
+    minimum=0,
+    maximum=1.0,
+    value=0.3,
+    step=0.01,
+    label="YOLO11 Confidence Threshold",
+)
+rf_detr_confidence_threshold_component = gr.Slider(
+    minimum=0,
+    maximum=1.0,
+    value=0.4,
+    step=0.01,
+    label="RF-DETR Confidence Threshold",
+)
 
 iou_threshold_component = gr.Slider(
     minimum=0,
@@ -179,10 +151,13 @@ with gr.Blocks() as demo:
     gr.Markdown(MARKDOWN)
     with gr.Accordion("Configuration", open=False):
         with gr.Row():
+            iou_threshold_component.render()
             yolo_v8_confidence_threshold_component.render()
             yolo_v9_confidence_threshold_component.render()
+        with gr.Row():
             yolo_v10_confidence_threshold_component.render()
-        iou_threshold_component.render()
+            yolo11_confidence_threshold_component.render()
+            rf_detr_confidence_threshold_component.render()
     with gr.Row():
         input_image_component = gr.Image(
             type='pil',
@@ -192,15 +167,23 @@ with gr.Blocks() as demo:
             type='pil',
             label='YOLOv8'
         )
-    with gr.Row():
         yolo_v9_output_image_component = gr.Image(
             type='pil',
             label='YOLOv9'
         )
+    with gr.Row():
         yolo_v10_output_image_component = gr.Image(
             type='pil',
             label='YOLOv10'
-            )
+        )
+        yolo11_output_image_component = gr.Image(
+            type='pil',
+            label='YOLO11'
+        )
+        rf_detr_output_image_component = gr.Image(
+            type='pil',
+            label='RF-DETR'
+        )
     submit_button_component = gr.Button(
         value='Submit',
         scale=1,
@@ -214,12 +197,16 @@ with gr.Blocks() as demo:
             yolo_v8_confidence_threshold_component,
             yolo_v9_confidence_threshold_component,
             yolo_v10_confidence_threshold_component,
+            yolo11_confidence_threshold_component,
+            rf_detr_confidence_threshold_component,
             iou_threshold_component
         ],
         outputs=[
             yolo_v8_output_image_component,
             yolo_v9_output_image_component,
-            yolo_v10_output_image_component
+            yolo_v10_output_image_component,
+            yolo11_output_image_component,
+            rf_detr_output_image_component
         ]
     )
 
@@ -230,12 +217,16 @@ with gr.Blocks() as demo:
             yolo_v8_confidence_threshold_component,
             yolo_v9_confidence_threshold_component,
             yolo_v10_confidence_threshold_component,
+            yolo11_confidence_threshold_component,
+            rf_detr_confidence_threshold_component,
             iou_threshold_component
         ],
         outputs=[
             yolo_v8_output_image_component,
             yolo_v9_output_image_component,
-            yolo_v10_output_image_component
+            yolo_v10_output_image_component,
+            yolo11_output_image_component,
+            rf_detr_output_image_component
         ]
     )
 

requirements.txt

@@ -1,5 +1,3 @@
-setuptools<70.0.0
-awscli==1.29.54
-gradio==4.19.2
-inference==0.11.2
-supervision==0.20.0
+gradio
+inference
+supervision