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README.md
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---
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title:
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colorFrom:
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sdk: gradio
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sdk_version: 3.
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app_file: app.py
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pinned: false
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license: openrail
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Battle Of The Image Classifiers
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emoji: 🐨
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colorFrom: green
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colorTo: gray
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sdk: gradio
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sdk_version: 3.16.1
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import gradio as gr
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from transformers import pipeline
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model_names = [
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"apple/mobilevit-small",
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"facebook/deit-base-patch16-224",
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"facebook/convnext-base-224",
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"google/vit-base-patch16-224",
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"google/mobilenet_v2_1.4_224",
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"microsoft/resnet-50",
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"microsoft/swin-base-patch4-window7-224",
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"microsoft/beit-base-patch16-224",
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"nvidia/mit-b0",
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"shi-labs/nat-base-in1k-224",
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"shi-labs/dinat-base-in1k-224",
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]
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def process(image_file, top_k):
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labels = []
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for m in model_names:
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p = pipeline("image-classification", model=m)
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pred = p(image_file)
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labels.append({x["label"]: x["score"] for x in pred[:top_k]})
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return labels
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# Inputs
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image = gr.Image(type="filepath", label="Upload an image")
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top_k = gr.Slider(minimum=1, maximum=5, step=1, value=5, label="Top k classes")
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# Output
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labels = [gr.Label(label=m) for m in model_names]
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description = "This Space lets you quickly compare the most popular image classifiers available on the hub, including the recent NAT and DINAT models. All of them have been fine-tuned on the ImageNet-1k dataset. Anecdotally, the three sample images have been generated with a Stable Diffusion model :)"
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iface = gr.Interface(
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theme="huggingface",
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description=description,
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layout="horizontal",
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fn=process,
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inputs=[image, top_k],
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outputs=labels,
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examples=[
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["bike.jpg", 5],
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["car.jpg", 5],
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["food.jpg", 5],
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],
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allow_flagging="never",
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)
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iface.launch()
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bike.jpg
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car.jpg
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food.jpg
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requirements.txt
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torch==1.13.1
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transformers>=4.25.1
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https://shi-labs.com/natten/wheels/cpu/torch1.13/natten-0.14.4%2Btorch1130cpu-cp38-cp38-linux_x86_64.whl
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