---
tags:
- pytorch_model_hub_mixin
- model_hub_mixin
license: mit
datasets:
- mlfoundations/datacomp_1b
base_model:
- apple/DFN5B-CLIP-ViT-H-14
---
## Official implementation of fine-tuned ViT-H/14 ProLIP on DataComp 1B

- This weight is a fine-tuned version of ViT-H/14 by Probabilistic Language-Image Pre-Training (ProLIP)
- Pre-trained weight
    - https://huggingface.co/apple/DFN5B-CLIP-ViT-H-14
- Fine-tuned dataset
    - DataComp 1B / Seen samples 1.28B
- Architectural difference
    - ProLIP text encoder uses the `[CLS]` token for pooling, while the original model uses the last token without specifying the `[CLS]` token.

### Overview
- Paper: https://arxiv.org/abs/2410.18857
- GitHub: https://github.com/naver-ai/prolip
- More models are available at https://huggingface.co/collections/SanghyukChun/prolip-6712595dfc87fd8597350291

### Performance overview
- Zero-shot ImageNet-1k top-1 accuracy: 79.4%
- Zero-shot ImageNet distribution shifts: 68.3%
- Zero-shot VTAB performance: 64.4%
- Zero-shot retrieval performance: 61.6%
- Average zero-shot performance on 38 tasks: 66.9%

```python
import requests
from PIL import Image

import torch
from prolip.model import ProLIPHF
from transformers import CLIPProcessor
from prolip.tokenizer import HFTokenizer

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
model = ProLIPHF.from_pretrained("SanghyukChun/ProLIP-ViT-H-14-FT-DC-1B-1_28M")
tokenizer = HFTokenizer("apple/DFN5B-CLIP-ViT-H-14", context_length=77)

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 couple of cats laying on top of a pink blanket.", "A man walks through a flooded road during a rainstorm", "photo"]
texts = tokenizer(texts)

outputs = model(image=inputs["pixel_values"], text=texts)

l2_logit = outputs["image_features"]["mean"] @ outputs["text_features"]["mean"].T
i_unc = torch.exp(outputs["image_features"]["std"]).sum(dim=-1)
t_unc = torch.exp(outputs["text_features"]["std"]).sum(dim=-1)
csd_logit = l2_logit - 0.5 * t_unc
csd_logit2 = l2_logit.T - 0.5 * i_unc
print("Mean-only image-to-text logits (by L2 distance):", l2_logit)
print("Uncertainty-aware image-to-text logits (by CSD):", csd_logit)
print("Uncertainty-aware text-to-image logits (by CSD):", csd_logit2.T)
print("Image uncertainty: ", i_unc)
print("Text uncertainty: ", t_unc)
```

```
@article{chun2024prolip,
  title={Probabilistic Language-Image Pre-Training},
  author={Chun, Sanghyuk and Kim, Wonjae and Park, Song and Yun, Sangdoo},
  journal={arXiv preprint arXiv:2410.18857},
  year={2024}
}
```