CVPR Demo Track

This organization invites participants to showoff conference papers on huggingface as a Gradio Web Demo

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Hugging Face Gradio CVPR event\n

\nCVPR organization is accepting Gradio demo submissions for CVPR papers from anyone for a chance to win prizes from Hugging Face, see prizes section and the leaderboard below. The deadline to submit demos is June 30th, 2022 (AOE Time Zone). For all partipants, feel free to submit Gradio demos for any CVPR paper for a chance to win prizes, you can submit demos for multiple papers. Find tutorial on getting started with Gradio on Hugging Face here and to get started with the new Gradio Blocks API here

Hugging Face Prizes

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• Top 5 spaces based on likes
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  • Swag from Hugging Face merch shop: t-shirt, hoodie, or mug of your choice
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LeaderBoard for Most Popular CVPR Spaces

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Hugging Face Spaces & Gradio for Showcasing your CVPR ‘22 Demo \n

\n In this tutorial, we will demonstrate how to showcase your demo with an easy to use web interface using the Gradio Python library and host it on Hugging Face Spaces so that conference attendees can easily find and try out your demos. Also, see https://gradio.app/introduction_to_blocks/, for a more flexible way to build Gradio Demos\n

🚀 Create a Gradio Demo from your Model\n

\nThe first step is to create a web demo from your model. As an example, we will be creating a demo from an image classification model (called model) which we will be uploading to Spaces. The full code for steps 1-4 can be found in this colab notebook.\n

1. Install the gradio library\n

\nAll you need to do is to run this in the terminal: pip install gradio\n

2. Define a function in your Python code that performs inference with your model on a data point and returns the prediction\n

\nHere’s we define our image classification model prediction function in PyTorch (any framework, like TensorFlow, scikit-learn, JAX, or a plain Python will work as well):\n

def predict(inp):\n\n
  inp = Image.fromarray(inp.astype('uint8'), 'RGB')
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  inp = transforms.ToTensor()(inp).unsqueeze(0)
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  with torch.no_grad():
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prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)\n
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  return {labels[i]: float(prediction[i]) for i in range(1000)}\n\n

3. Then create a Gradio Interface using the function and the appropriate input and output types\n

\nFor the image classification model from Step 2, it would like like this:\n

\ninputs = gr.inputs.Image()\n\n
outputs = gr.outputs.Label(num_top_classes=3)
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io = gr.Interface(fn=predict, inputs=inputs, outputs=outputs)\n\n

\nIf you need help creating a Gradio Interface for your model, check out the Gradio Getting Started guide.\n

4. Then launch() you Interface to confirm that it runs correctly locally (or wherever you are running Python)\n

\nio.launch() \n\n

\nYou should see a web interface like the following where you can drag and drop your data points and see the predictions:\n