PCMEPP-ViT-B-16-CC3M-12M-RedCaps

Official implementation of PCME++ pre-trained model on CC3M, CC12M and RedCaps.

Zero-shot ImageNet-1k top-1 accuracy: 34.642% (slightly better than the paper score, 34.22%)

• Paper: https://openreview.net/forum?id=ft1mr3WlGM
• GitHub: https://github.com/naver-ai/pcmepp
• Check a better version with ImageNet-1k top-1 accuracy 41.812% (mean-only ZS classification) at SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps-256M

import requests
from PIL import Image

import torch
from transformers import CLIPProcessor

# Check hf_models code here: https://github.com/naver-ai/pcmepp/tree/main/hf_models
from hf_models import HfPCMEPPModel, tokenize


processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
# IN-top1: 34.64%
model = HfPCMEPPModel.from_pretrained("SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps")
# IN-top1: 41.81%
# model = HfPCMEPPModel.from_pretrained("SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps-256M")


url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt", padding=True)
texts = ["a photo of a cat", "a photo of a dog"]
texts = tokenize(texts)

outputs = model(images=inputs["pixel_values"], texts=texts)
print("Logits:", outputs["image_features"] @ outputs["text_features"].T)
print("Image uncertainty: ", torch.exp(outputs["image_stds"]).mean(dim=-1))
print("Text uncertainty: ", torch.exp(outputs["text_stds"]).mean(dim=-1))

@inproceedings{
chun2024pcmepp,
title={Improved Probabilistic Image-Text Representations},
author={Sanghyuk Chun},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=ft1mr3WlGM}
}