Instructions to use fassabilf/clipkd-released-ViT-T-16-baseline-cc12m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use fassabilf/clipkd-released-ViT-T-16-baseline-cc12m with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:fassabilf/clipkd-released-ViT-T-16-baseline-cc12m') tokenizer = open_clip.get_tokenizer('hf-hub:fassabilf/clipkd-released-ViT-T-16-baseline-cc12m') - Notebooks
- Google Colab
- Kaggle
Model card for clipkd-released-ViT-T-16-baseline-cc12m
ViT-T/16 baseline student trained on CC3M+12M (no KD). ImageNet acc: 30.55%
Model Details
- Architecture: ViT-T-16
- Method: CLIP-KD
- Training Data: CC3M+CC12M
- ImageNet Top-1: 30.6%
- Original Repo: https://github.com/winycg/CLIP-KD
- Paper: CLIP-KD: An Empirical Study of CLIP Model Distillation (CVPR 2024)
Model Usage
import open_clip
model, _, preprocess = open_clip.create_model_and_transforms(
'hf-hub:fassabilf/clipkd-released-ViT-T-16-baseline-cc12m'
)
tokenizer = open_clip.get_tokenizer('hf-hub:fassabilf/clipkd-released-ViT-T-16-baseline-cc12m')
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