Updated with YOLO11

README.md

@@ -1,5 +1,5 @@
 ---
-title: Segment Something
+title: Segmentation-Playground
 emoji: 🖼️
 colorFrom: green
 colorTo: red

app.py

@@ -5,34 +5,66 @@ import numpy as np
 import supervision as sv
 from inference import get_model
 import warnings
+from ultralytics import YOLO
 
 warnings.filterwarnings("ignore")
 
 MARKDOWN = """
-<h1 style='text-align: center'>Segment Something 🖼️</h1>
-Welcome to Segment Something! Just a simple demo to showcase the instance segmentation capabilities of various YOLOv8 segmentation models. 🚀🔍👀
+<h1 style='text-align: left'>Segmentation-Playground 🖼️</h1>
+Welcome to Segmentation-Playground! This demo showcases the segmentation capabilities of various YOLO models pre-trained on the COCO Dataset. 🚀🔍👀
 
 A simple project just for fun for on the go instance segmentation. 🎉
 
 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>
+- **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, 0.5],
+    ['https://media.roboflow.com/supervision/image-examples/vehicles.png', 0.3, 0.3, 0.3, 0.5],
+    ['https://media.roboflow.com/supervision/image-examples/basketball-1.png', 0.3, 0.3, 0.3, 0.5],
 ]
 
-YOLO_V8N_MODEL = get_model(model_id="yolov8n-seg-640")
-YOLO_V8S_MODEL = get_model(model_id="yolov8s-seg-640")
-YOLO_V8M_MODEL = get_model(model_id="yolov8m-seg-640")
+YOLO_V8S_MODEL = YOLO("yolov8m-seg.pt")
+YOLO_V9S_MODEL = YOLO("yolov9e-seg.pt")
+YOLO_11S_MODEL = YOLO("yolo11m-seg.pt")
 
-LABEL_ANNOTATORS = sv.LabelAnnotator(text_color=sv.Color.black())
+LABEL_ANNOTATORS = sv.LabelAnnotator()
 MASK_ANNOTATORS = sv.MaskAnnotator()
-BOUNDING_BOX_ANNOTATORS = sv.BoundingBoxAnnotator()
+BOUNDING_BOX_ANNOTATORS = sv.BoxAnnotator()
 
 
 def detect_and_annotate(
@@ -42,12 +74,12 @@ 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([
@@ -84,11 +116,11 @@ def process_image(
         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_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_V9S_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_11S_MODEL, input_image, yolo_v10_confidence_threshold, iou_threshold)
 
     return (
         yolo_v8n_annotated_image,
@@ -102,7 +134,7 @@ yolo_v8N_confidence_threshold_component = gr.Slider(
     maximum=1.0,
     value=0.3,
     step=0.01,
-    label="YOLOv8N Confidence Threshold",
+    label="YOLOv8m Confidence Threshold",
     info=(
         "The confidence threshold for the YOLO model. Lower the threshold to "
         "reduce false negatives, enhancing the model's sensitivity to detect "
@@ -115,7 +147,7 @@ yolo_v8S_confidence_threshold_component = gr.Slider(
     maximum=1.0,
     value=0.3,
     step=0.01,
-    label="YOLOv8S Confidence Threshold",
+    label="YOLOv9e Confidence Threshold",
     info=(
         "The confidence threshold for the YOLO model. Lower the threshold to "
         "reduce false negatives, enhancing the model's sensitivity to detect "
@@ -128,7 +160,7 @@ yolo_v8M_confidence_threshold_component = gr.Slider(
     maximum=1.0,
     value=0.3,
     step=0.01,
-    label="YOLOv8M Confidence Threshold",
+    label="YOLO11m Confidence Threshold",
     info=(
         "The confidence threshold for the YOLO model. Lower the threshold to "
         "reduce false negatives, enhancing the model's sensitivity to detect "
@@ -166,16 +198,16 @@ with gr.Blocks() as demo:
         )
         yolo_v8n_output_image_component = gr.Image(
             type='pil',
-            label='YOLOv8N'
+            label='YOLOv8m'
         )
     with gr.Row():
         yolo_v8s_output_image_component = gr.Image(
             type='pil',
-            label='YOLOv8S'
+            label='YOLOv9e'
         )
         yolo_v8m_output_image_component = gr.Image(
             type='pil',
-            label='YOLOv8M'
+            label='YOLO11m'
             )
     submit_button_component = gr.Button(
         value='Submit',

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
+dill
+timm