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import gradio as gr |
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from ultralytics import YOLOv10 |
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import supervision as sv |
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import spaces |
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from huggingface_hub import hf_hub_download |
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def download_models(model_id): |
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hf_hub_download("kadirnar/yolov10", filename=f"{model_id}", local_dir=f"./") |
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return f"./{model_id}" |
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MODEL_PATH = 'yolov10n.pt' |
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model = YOLOv10(MODEL_PATH) |
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box_annotator = sv.BoxAnnotator() |
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@spaces.GPU(duration=200) |
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def yolov10_inference(image, model_id, image_size, conf_threshold, iou_threshold): |
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model_path = download_models(model_id) |
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results = model(source=image, imgsz=image_size, iou=iou_threshold, conf=conf_threshold, verbose=False)[0] |
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detections = sv.Detections.from_ultralytics(results) |
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labels = [ |
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f"{model.model.names[class_id]} {confidence:.2f}" |
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for class_id, confidence in zip(detections.class_id, detections.confidence) |
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] |
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annotated_image = box_annotator.annotate(image, detections=detections, labels=labels) |
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return annotated_image |
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def app(): |
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with gr.Blocks(): |
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with gr.Row(): |
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with gr.Column(): |
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image = gr.Image(type="numpy", label="Image") |
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model_id = gr.Dropdown( |
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label="Model", |
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choices=[ |
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"yolov10n.pt", |
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"yolov10s.pt", |
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"yolov10m.pt", |
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"yolov10b.pt", |
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"yolov10x.pt", |
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], |
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value="yolov10s.pt", |
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) |
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image_size = gr.Slider( |
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label="Image Size", |
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minimum=320, |
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maximum=1280, |
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step=32, |
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value=640, |
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) |
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conf_threshold = gr.Slider( |
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label="Confidence Threshold", |
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minimum=0.1, |
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maximum=1.0, |
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step=0.1, |
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value=0.25, |
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) |
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iou_threshold = gr.Slider( |
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label="IoU Threshold", |
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minimum=0.1, |
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maximum=1.0, |
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step=0.1, |
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value=0.45, |
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) |
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yolov10_infer = gr.Button(value="Detect Objects") |
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with gr.Column(): |
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output_image = gr.Image(type="numpy", label="Annotated Image") |
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yolov10_infer.click( |
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fn=yolov10_inference, |
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inputs=[ |
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image, |
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model_id, |
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image_size, |
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conf_threshold, |
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iou_threshold, |
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], |
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outputs=[output_image], |
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) |
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gr.Examples( |
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examples=[ |
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[ |
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"huggingface.jpg", |
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"yolov10m.pt", |
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640, |
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0.25, |
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0.45, |
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], |
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[ |
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"zidane.jpg", |
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"yolov10b.pt", |
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640, |
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0.25, |
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0.45, |
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], |
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], |
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fn=yolov10_inference, |
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inputs=[ |
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image, |
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model_id, |
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image_size, |
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conf_threshold, |
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iou_threshold, |
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], |
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outputs=[output_image], |
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cache_examples=True, |
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) |
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gradio_app = gr.Blocks() |
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with gradio_app: |
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gr.Markdown( |
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""" |
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# YOLOv10: State-of-the-Art Object Detection |
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""" |
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) |
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gr.Markdown( |
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""" |
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Detect objects in images using the YOLOv10 model. Select a pre-trained model, adjust the inference settings, and upload an image to see the detected objects. |
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""" |
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) |
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with gr.Row(): |
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gr.Markdown( |
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""" |
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Follow me for more projects and updates: |
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- [Twitter](https://twitter.com/kadirnar_ai) |
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- [GitHub](https://github.com/kadirnar) |
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- [LinkedIn](https://www.linkedin.com/in/kadir-nar/) |
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- [HuggingFace](https://www.huggingface.co/kadirnar/) |
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""" |
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) |
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app() |
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gradio_app.launch(debug=True) |
