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import gradio as gr |
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from transformers import ImageClassificationPipeline, PerceiverForImageClassificationConvProcessing, PerceiverFeatureExtractor |
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import torch |
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torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg') |
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torch.hub.download_url_to_file('https://storage.googleapis.com/perceiver_io/dalmation.jpg', 'dog.jpg') |
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feature_extractor = PerceiverFeatureExtractor.from_pretrained("deepmind/vision-perceiver-conv") |
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model = PerceiverForImageClassificationConvProcessing.from_pretrained("deepmind/vision-perceiver-conv") |
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image_pipe = ImageClassificationPipeline(model=model, feature_extractor=feature_extractor) |
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def classify_image(image): |
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results = image_pipe(image) |
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output = {} |
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for prediction in results: |
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predicted_label = prediction['label'] |
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score = prediction['score'] |
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output[predicted_label] = score |
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return output |
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image = gr.inputs.Image(type="pil") |
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label = gr.outputs.Label(num_top_classes=5) |
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examples = [["cats.jpg"], ["dog.jpg"]] |
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title = "Interactive demo: Perceiver for image classification" |
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description = "Demo for classifying images with Perceiver IO. To use it, simply upload an image or use the example images below and click 'submit' to let the model predict the 5 most probable ImageNet classes. Results will show up in a few seconds." |
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2107.14795'>Perceiver IO: A General Architecture for Structured Inputs & Outputs</a> | <a href='https://deepmind.com/blog/article/building-architectures-that-can-handle-the-worlds-data/'>Official blog</a></p>" |
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gr.Interface(fn=classify_image, inputs=image, outputs=label, title=title, description=description, examples=examples, enable_queue=True).launch(debug=True) |
