import gradio as gr from ultralytics import YOLOv10 import supervision as sv import spaces from huggingface_hub import hf_hub_download def download_models(model_id): hf_hub_download("BoukamchaSmartVisions/Yolov10", filename=f"{model_id}", local_dir=f"./") return f"./{model_id}" box_annotator = sv.BoxAnnotator() category_dict = { 0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush' } @spaces.GPU(duration=200) def yolov10_inference(image, model_id, image_size, conf_threshold, iou_threshold): model_path = download_models(model_id) model = YOLOv10(model_path) results = model(source=image, imgsz=image_size, iou=iou_threshold, conf=conf_threshold, verbose=False)[0] detections = sv.Detections.from_ultralytics(results) labels = [ f"{category_dict[class_id]} {confidence:.2f}" for class_id, confidence in zip(detections.class_id, detections.confidence) ] annotated_image = box_annotator.annotate(image, detections=detections, labels=labels) return annotated_image def app(): with gr.Blocks(): with gr.Row(): with gr.Column(): image = gr.Image(type="numpy", label="Image") model_id = gr.Dropdown( label="Model", choices=[ "yolov10n.pt", "yolov10s.pt", "yolov10m.pt", "yolov10b.pt", "yolov10l.pt", "yolov10x.pt", ], value="yolov10m.pt", ) image_size = gr.Slider( label="Image Size", minimum=320, maximum=1280, step=32, value=640, ) conf_threshold = gr.Slider( label="Confidence Threshold", minimum=0.1, maximum=1.0, step=0.1, value=0.25, ) iou_threshold = gr.Slider( label="IoU Threshold", minimum=0.1, maximum=1.0, step=0.1, value=0.45, ) yolov10_infer = gr.Button(value="Detect Objects") with gr.Column(): output_image = gr.Image(type="numpy", label="Annotated Image") yolov10_infer.click( fn=yolov10_inference, inputs=[ image, model_id, image_size, conf_threshold, iou_threshold, ], outputs=[output_image], ) gr.Examples( examples=[ [ "Animals_persones.jpg", "yolov10x.pt", 640, 0.25, 0.45, ], [ "collage-horses-other-pets-white.jpg", "yolov10m.pt", 640, 0.25, 0.45, ], [ "Ville.png", "yolov10b.pt", 640, 0.25, 0.45, ], ], fn=yolov10_inference, inputs=[ image, model_id, image_size, conf_threshold, iou_threshold, ], outputs=[output_image], cache_examples=True, ) gradio_app = gr.Blocks() with gradio_app: gr.HTML( """

YOLOv10: Real-Time End-to-End Object Detection

""") gr.HTML( """

Follow me for more! Github | Linkedin | HuggingFace

""") with gr.Row(): with gr.Column(): app() gradio_app.launch(debug=True)