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README.md
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- model_hub_mixin
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---
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- model_hub_mixin
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---
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### Official implementation of PCME++ pre-trained model on CC3M, CC12M and RedCaps.
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Zero-shot ImageNet-1k top-1 accuracy: 41.812% (with longer training iterations than the previous version)
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- Paper: https://openreview.net/forum?id=ft1mr3WlGM
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- GitHub: https://github.com/naver-ai/pcmepp
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- Check the official version with ImageNet-1k top-1 accuracy 34.642% (mean-only ZS classification) at [SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps](https://huggingface.co/SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps)
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```python
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import requests
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from PIL import Image
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import torch
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from transformers import CLIPProcessor
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# Check hf_models code here: https://github.com/naver-ai/pcmepp/tree/main/hf_models
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from hf_models import HfPCMEPPModel, tokenize
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processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
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# IN-top1: 34.64%
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# model = HfPCMEPPModel.from_pretrained("SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps")
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# IN-top1: 41.81%
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model = HfPCMEPPModel.from_pretrained("SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps-256M")
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = processor(images=image, return_tensors="pt", padding=True)
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texts = ["a photo of a cat", "a photo of a dog"]
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texts = tokenize(texts)
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outputs = model(images=inputs["pixel_values"], texts=texts)
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print("Logits:", outputs["image_features"] @ outputs["text_features"].T)
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print("Image uncertainty: ", torch.exp(outputs["image_stds"]).mean(dim=-1))
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print("Text uncertainty: ", torch.exp(outputs["text_stds"]).mean(dim=-1))
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```
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```
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@inproceedings{
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chun2024pcmepp,
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title={Improved Probabilistic Image-Text Representations},
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author={Sanghyuk Chun},
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booktitle={The Twelfth International Conference on Learning Representations},
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year={2024},
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url={https://openreview.net/forum?id=ft1mr3WlGM}
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}
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```
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