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Runtime error
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| # Images | |
| torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/06/15/01/11/soccer-1457988_1280.jpg', 'soccer.jpg') | |
| torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/11/21/14/31/vw-bus-1845719_1280.jpg', 'bus.jpg') | |
| # Model | |
| model = torch.hub.load('ultralytics/yolov3', 'yolov3') # or yolov3-spp, yolov3-tiny, custom | |
| def yolo(im, size=1920): | |
| g = (size / max(im.size)) # gain | |
| im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) # resize | |
| results = model(im) # inference | |
| results.render() # updates results.imgs with boxes and labels | |
| return Image.fromarray(results.imgs[0]) | |
| inputs = gr.inputs.Image(type='pil', label="Original Image") | |
| outputs = gr.outputs.Image(type="pil", label="Output Image") | |
| title = "YOLOv3" | |
| description = "YOLOv3 Gradio demo for object detection. Upload an image or click an example image to use." | |
| article = "<p style='text-align: center'>YOLOv3 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov3' target='_blank'>Source code</a> |<a href='https://apps.apple.com/app/id1452689527' target='_blank'>iOS App</a></p>" | |
| examples = [['soccer.jpg'], ['bus.jpg']] | |
| gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch( | |
| debug=True) |