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clip-vit-base-patch32-fullcc2.5b
cs-giung/clip-vit-base-patch32-fullcc2.5b
clip-vit-base-patch32-fullcc2.5b is a zero-shot image classification model from cs-giung. Use it for the zero-shot image classification task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as cc-by-nc-4.0.
Contrastive Language-Image Pretraining (CLIP) model pre-trained on 2.5 billion data points of CommonCrawl at resolution 224x224. It was introduced in the paper Learning Transferable Visual Models From Natural Language…
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From the Hugging Face model README
CLIP
Contrastive Language-Image Pretraining (CLIP) model pre-trained on 2.5 billion data points of CommonCrawl at resolution 224x224. It was introduced in the paper Learning Transferable Visual Models From Natural Language Supervision and further reproduced in the follow-up paper Demystifying CLIP Data.
The weights were converted from the b32_fullcc2.5b.pt file presented in the original repository.
- Task
- Zero-Shot Image Classification
- Library
- transformers
- Type
- clip
- License
- cc-by-nc-4.0
- Created
- Jul 7, 2024
- Updated
- Jul 7, 2024