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update app.py
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README.md
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README.md
@ -11,7 +11,7 @@ Official PyTorch implementation of **YOLOv10**.
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[YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458).\
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Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, and Guiguang Ding\
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[](https://arxiv.org/abs/2405.14458) <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb#scrollTo=SaKTSzSWnG7s"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> [](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) [](https://huggingface.co/spaces/kadirnar/Yolov10) [](https://huggingface.co/spaces/Xenova/yolov10-web)
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[](https://arxiv.org/abs/2405.14458) <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb#scrollTo=SaKTSzSWnG7s"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> [](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) [](https://huggingface.co/spaces/jameslahm/YOLOv10) [](https://huggingface.co/spaces/kadirnar/Yolov10) [](https://huggingface.co/spaces/Xenova/yolov10-web)
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<details>
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<summary>
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@ -23,7 +23,7 @@ Over the past years, YOLOs have emerged as the predominant paradigm in the field
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## Notes
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- 2024/05/31: Please use the [exported format](https://github.com/THU-MIG/yolov10?tab=readme-ov-file#export) for benchmark. In the non-exported format, e.g., pytorch, the speed of YOLOv10 is biased because the unnecessary `cv2` and `cv3` operations in the `v10Detect` are executed during inference.
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- 2024/05/30: We provide [some clarifications and suggestions](https://github.com/THU-MIG/yolov10/issues/136) for detecting smaller objects or objects in the distance with YOLOv10. Thanks to [SkalskiP](https://github.com/SkalskiP)!
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- 2024/05/27: We have updated the [checkpoints](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) with other attributes, like class names and training args, for ease of use.
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- 2024/05/27: We have updated the [checkpoints](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) with class names, for ease of use.
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## UPDATES 🔥
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- 2024/06/01: Thanks to [ErlanggaYudiPradana](https://github.com/rlggyp) for the integration with [C++ | OpenVINO | OpenCV](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference)
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@ -97,12 +97,10 @@ model = YOLOv10()
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# If you want to finetune the model with pretrained weights, you could load the
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# pretrained weights like below
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# model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
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# Or
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# or
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# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
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model.train(data='coco.yaml', epochs=500, batch=256, imgsz=640)
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# Note that you can upload your trained model to HuggingFace Hub like below
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# model.push_to_hub("reponame", config={"model": "yolov10n/s/m/b/l/x.yaml"})
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```
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## Push to hub to 🤗
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@ -143,7 +141,7 @@ yolo predict model=yolov10n/s/m/b/l/x.onnx
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# End-to-End TensorRT
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yolo export model=yolov10n/s/m/b/l/x.pt format=engine half=True simplify opset=13 workspace=16
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# Or
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# or
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trtexec --onnx=yolov10n/s/m/b/l/x.onnx --saveEngine=yolov10n/s/m/b/l/x.engine --fp16
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# Predict with TensorRT
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yolo predict model=yolov10n/s/m/b/l/x.engine
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60
app.py
60
app.py
@ -1,15 +1,23 @@
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# Ackownledgement: https://huggingface.co/spaces/kadirnar/Yolov10/blob/main/app.py
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# Thanks to @kadirnar
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import PIL.Image as Image
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import gradio as gr
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from ultralytics import YOLOv10
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def yolov10_inference(image, image_size, conf_threshold):
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model = YOLOv10.from_pretrained("jameslahm/yolov10n")
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def predict_image(img, model_id, image_size, conf_threshold):
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model = YOLOv10.from_pretrained(f'jameslahm/{model_id}')
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results = model.predict(
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source=img,
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conf=conf_threshold,
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show_labels=True,
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show_conf=True,
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imgsz=image_size,
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)
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model.predict(source=image, imgsz=image_size, conf=conf_threshold, save=True)
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for r in results:
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im_array = r.plot()
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im = Image.fromarray(im_array[..., ::-1])
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return model.predictor.plotted_img[:, :, ::-1]
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return im
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def app():
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with gr.Blocks():
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@ -20,14 +28,14 @@ def app():
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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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"yolov10l.pt",
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"yolov10x.pt",
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"yolov10n",
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"yolov10s",
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"yolov10m",
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"yolov10b",
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"yolov10l",
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"yolov10x",
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],
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value="yolov10s.pt",
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value="yolov10m",
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)
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image_size = gr.Slider(
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label="Image Size",
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@ -40,16 +48,16 @@ def app():
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label="Confidence Threshold",
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minimum=0.0,
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maximum=1.0,
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step=0.1,
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step=0.05,
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value=0.25,
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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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output_image = gr.Image(type="pil", label="Annotated Image")
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yolov10_infer.click(
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fn=yolov10_inference,
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fn=predict_image,
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inputs=[
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image,
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model_id,
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@ -63,18 +71,18 @@ def app():
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examples=[
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[
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"ultralytics/assets/bus.jpg",
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"yolov10s.pt",
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"yolov10s",
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640,
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0.25,
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],
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[
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"ultralytics/assets/zidane.jpg",
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"yolov10s.pt",
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"yolov10s",
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640,
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0.25,
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],
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],
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fn=yolov10_inference,
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fn=predict_image,
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inputs=[
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image,
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model_id,
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@ -82,7 +90,7 @@ def app():
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conf_threshold,
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],
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outputs=[output_image],
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cache_examples=True,
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cache_examples='lazy',
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)
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gradio_app = gr.Blocks()
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@ -93,8 +101,14 @@ with gradio_app:
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YOLOv10: Real-Time End-to-End Object Detection
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</h1>
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""")
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gr.HTML(
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"""
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<h3 style='text-align: center'>
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<a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a>
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</h3>
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""")
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with gr.Row():
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with gr.Column():
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app()
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gradio_app.launch(debug=True)
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if __name__ == '__main__':
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gradio_app.launch()
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