Added YOLO11

README.md

@@ -1,5 +1,5 @@
 ---
-title: Detect Something
+title: YOLO-Playground
 emoji: 📈
 colorFrom: blue
 colorTo: green

app.py

@@ -3,32 +3,76 @@ from typing import Tuple
 import gradio as gr
 import numpy as np
 import supervision as sv
-from inference import get_model
+from ultralytics import YOLO
 
 MARKDOWN = """
-<h1 style='text-align: center'>Detect Something 📈</h1>
-Welcome to Detect Something! Just a simple demo to showcase the detection capabilities of various YOLOv8 models. 🚀🔍👀
+<h1 style='text-align: left'>YOLO-Playground 📈</h1>
+Welcome to YOLO-Playground! This demo showcases the detection capabilities of various YOLO models pre-trained on the COCO Dataset. 🚀🔍👀
 
 A simple project just for fun for on the go object detection. 🎉
 
 Inspired from YOLO-ARENA by SkalskiP. 🙏
 
-Powered by Roboflow [Inference](https://github.com/roboflow/inference) and 
-[Supervision](https://github.com/roboflow/supervision). 🔥
+- **YOLOv8**
+  <div style="display: flex; align-items: center;">
+    <a href="https://docs.ultralytics.com/models/yolov8/" 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>
+- **YOLO11**
+<div style="display: flex; align-items: center;">
+    <a href="https://docs.ultralytics.com/models/yolo11/" 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>
+
+Powered by Roboflow [Inference](https://github.com/roboflow/inference), 
+[Supervision](https://github.com/roboflow/supervision) and [Ultralytics](https://github.com/ultralytics/ultralytics).🔥
 """
 
 IMAGE_EXAMPLES = [
-    ['https://media.roboflow.com/supervision/image-examples/people-walking.png', 0.3, 0.3, 0.3],
-    ['https://media.roboflow.com/supervision/image-examples/vehicles.png', 0.3, 0.3, 0.3],
-    ['https://media.roboflow.com/supervision/image-examples/basketball-1.png', 0.3, 0.3, 0.3],
+    ['https://media.roboflow.com/supervision/image-examples/people-walking.png', 0.3, 0.3, 0.3, 0.3, 0.5],
+    ['https://media.roboflow.com/supervision/image-examples/vehicles.png', 0.3, 0.3, 0.3, 0.3, 0.5],
+    ['https://media.roboflow.com/supervision/image-examples/basketball-1.png', 0.3, 0.3, 0.3, 0.3, 0.5],
 ]
 
-YOLO_V8N_MODEL = get_model(model_id="yolov8n-640")
-YOLO_V8S_MODEL = get_model(model_id="yolov8s-640")
-YOLO_V8M_MODEL = get_model(model_id="yolov8m-640")
+YOLO_V8S_MODEL = YOLO("yolov8s.pt")
+YOLO_V9S_MODEL = YOLO("yolov9s.pt")
+YOLO_V10S_MODEL = YOLO("yolov10s.pt")
+YOLO_11S_MODEL = YOLO("yolo11s.pt")
 
-LABEL_ANNOTATORS = sv.LabelAnnotator(text_color=sv.Color.black())
-BOUNDING_BOX_ANNOTATORS = sv.BoundingBoxAnnotator()
+LABEL_ANNOTATORS = sv.LabelAnnotator()
+BOUNDING_BOX_ANNOTATORS = sv.BoxAnnotator()
 
 
 def detect_and_annotate(
@@ -38,12 +82,12 @@ def detect_and_annotate(
     iou_threshold: float,
     class_id_mapping: dict = None
 ) -> np.ndarray:
-    result = model.infer(
+    result = model(
         input_image,
-        confidence=confidence_threshold,
-        iou_threshold=iou_threshold
+        conf=confidence_threshold,
+        iou=iou_threshold
     )[0]
-    detections = sv.Detections.from_inference(result)
+    detections = sv.Detections.from_ultralytics(result)
 
     if class_id_mapping:
         detections.class_id = np.array([
@@ -71,32 +115,49 @@ def process_image(
     yolo_v8_confidence_threshold: float,
     yolo_v9_confidence_threshold: float,
     yolo_v10_confidence_threshold: float,
+    yolov11_confidence_threshold: float,
     iou_threshold: float
 ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
     # Validate iou_threshold before using it
     if iou_threshold is None or not isinstance(iou_threshold, float):
         iou_threshold = 0.3  # Default value, adjust as necessary
 
-    yolo_v8n_annotated_image = detect_and_annotate(
-        YOLO_V8N_MODEL, input_image, yolo_v8_confidence_threshold, iou_threshold)
     yolo_v8s_annotated_image = detect_and_annotate(
-        YOLO_V8S_MODEL, input_image, yolo_v9_confidence_threshold, iou_threshold)
-    yolo_8m_annotated_image = detect_and_annotate(
-        YOLO_V8M_MODEL, input_image, yolo_v10_confidence_threshold, iou_threshold)
+        YOLO_V8S_MODEL, input_image, yolo_v8_confidence_threshold, iou_threshold)
+    yolo_v9s_annotated_image = detect_and_annotate(
+        YOLO_V9S_MODEL, input_image, yolo_v9_confidence_threshold, iou_threshold)
+    yolo_v10s_annotated_image = detect_and_annotate(
+        YOLO_V10S_MODEL, input_image, yolo_v10_confidence_threshold, iou_threshold)
+    yolo_11s_annnotated_image = detect_and_annotate(
+        YOLO_11S_MODEL, input_image, yolov11_confidence_threshold, iou_threshold)
 
     return (
-        yolo_v8n_annotated_image,
         yolo_v8s_annotated_image,
-        yolo_8m_annotated_image
+        yolo_v9s_annotated_image,
+        yolo_v10s_annotated_image,
+        yolo_11s_annnotated_image
     )
 
+yolo_v8s_confidence_threshold_component = gr.Slider(
+    minimum=0,
+    maximum=1.0,
+    value=0.3,
+    step=0.01,
+    label="YOLOv8s 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_v8N_confidence_threshold_component = gr.Slider(
+
+yolo_v9s_confidence_threshold_component = gr.Slider(
     minimum=0,
     maximum=1.0,
     value=0.3,
     step=0.01,
-    label="YOLOv8N Confidence Threshold",
+    label="YOLOv9s Confidence Threshold",
     info=(
         "The confidence threshold for the YOLO model. Lower the threshold to "
         "reduce false negatives, enhancing the model's sensitivity to detect "
@@ -104,12 +165,12 @@ yolo_v8N_confidence_threshold_component = gr.Slider(
         "positives, preventing the model from identifying objects it shouldn't."
     ))
 
-yolo_v8S_confidence_threshold_component = gr.Slider(
+yolo_v10s_confidence_threshold_component = gr.Slider(
     minimum=0,
     maximum=1.0,
     value=0.3,
     step=0.01,
-    label="YOLOv8S Confidence Threshold",
+    label="YOLOv10s Confidence Threshold",
     info=(
         "The confidence threshold for the YOLO model. Lower the threshold to "
         "reduce false negatives, enhancing the model's sensitivity to detect "
@@ -117,12 +178,12 @@ yolo_v8S_confidence_threshold_component = gr.Slider(
         "positives, preventing the model from identifying objects it shouldn't."
     ))
 
-yolo_v8M_confidence_threshold_component = gr.Slider(
+yolo_11s_confidence_threshold_component = gr.Slider(
     minimum=0,
     maximum=1.0,
     value=0.3,
     step=0.01,
-    label="YOLOv8M Confidence Threshold",
+    label="YOLO11s Confidence Threshold",
     info=(
         "The confidence threshold for the YOLO model. Lower the threshold to "
         "reduce false negatives, enhancing the model's sensitivity to detect "
@@ -149,27 +210,33 @@ with gr.Blocks() as demo:
     gr.Markdown(MARKDOWN)
     with gr.Accordion("Configuration", open=False):
         with gr.Row():
-            yolo_v8N_confidence_threshold_component.render()
-            yolo_v8S_confidence_threshold_component.render()
-            yolo_v8M_confidence_threshold_component.render()
+            yolo_v8s_confidence_threshold_component.render()
+            yolo_v9s_confidence_threshold_component.render()
+            yolo_v10s_confidence_threshold_component.render()
+            yolo_11s_confidence_threshold_component.render()
         iou_threshold_component.render()
     with gr.Row():
         input_image_component = gr.Image(
             type='pil',
             label='Input'
         )
-        yolo_v8n_output_image_component = gr.Image(
+    with gr.Row():
+        yolo_v8s_output_image_component = gr.Image(
+            type='pil',
+            label='YOLOv8s'
+        )
+        yolo_v9s_output_image_component = gr.Image(
             type='pil',
-            label='YOLOv8N'
+            label='YOLOv9s'
         )
     with gr.Row():
-        yolo_v8s_output_image_component = gr.Image(
+        yolo_v10s_output_image_component = gr.Image(
             type='pil',
-            label='YOLOv8S'
+            label='YOLOv10s'
         )
-        yolo_v8m_output_image_component = gr.Image(
+        yolo_11s_output_image_component = gr.Image(
             type='pil',
-            label='YOLOv8M'
+            label='YOLO11s'
             )
     submit_button_component = gr.Button(
         value='Submit',
@@ -181,15 +248,17 @@ with gr.Blocks() as demo:
         examples=IMAGE_EXAMPLES,
         inputs=[
             input_image_component,
-            yolo_v8N_confidence_threshold_component,
-            yolo_v8S_confidence_threshold_component,
-            yolo_v8M_confidence_threshold_component,
+            yolo_v8s_confidence_threshold_component,
+            yolo_v9s_confidence_threshold_component,
+            yolo_v10s_confidence_threshold_component,
+            yolo_11s_confidence_threshold_component,
             iou_threshold_component
         ],
         outputs=[
-            yolo_v8n_output_image_component,
             yolo_v8s_output_image_component,
-            yolo_v8m_output_image_component
+            yolo_v9s_output_image_component,
+            yolo_v10s_output_image_component,
+            yolo_11s_output_image_component
         ]
     )
 
@@ -197,15 +266,17 @@ with gr.Blocks() as demo:
         fn=process_image,
         inputs=[
             input_image_component,
-            yolo_v8N_confidence_threshold_component,
-            yolo_v8S_confidence_threshold_component,
-            yolo_v8M_confidence_threshold_component,
+            yolo_v8s_confidence_threshold_component,
+            yolo_v9s_confidence_threshold_component,
+            yolo_v10s_confidence_threshold_component,
+            yolo_11s_confidence_threshold_component,
             iou_threshold_component
         ],
         outputs=[
-            yolo_v8n_output_image_component,
             yolo_v8s_output_image_component,
-            yolo_v8m_output_image_component
+            yolo_v9s_output_image_component,
+            yolo_v10s_output_image_component,
+            yolo_11s_output_image_component
         ]
     )
 

requirements.txt

@@ -1,5 +1,8 @@
 setuptools<70.0.0
 awscli==1.29.54
-gradio==4.19.2
-inference==0.13.0
-supervision==0.20.0
+gradio
+inference
+supervision
+ultralytics
+pill
+timm