Image Feature Extraction
OpenCLIP
PyTorch
English
fashion
image-retrieval
image-to-image
siglip
lookbench
embedding
vision-only
fp16
compressed
Instructions to use HopitAI/moda-fashion-vision-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use HopitAI/moda-fashion-vision-fp16 with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:HopitAI/moda-fashion-vision-fp16') tokenizer = open_clip.get_tokenizer('hf-hub:HopitAI/moda-fashion-vision-fp16') - Notebooks
- Google Colab
- Kaggle
Commit History
-
remove dataset-specific training references pending licensing review
Verified signature9f3358c -
revert: remove DeepFashion2 citation block pending review
Verified signaturea43d8ba -
cite DeepFashion2 (CVPR 2019) as required by the dataset agreement
Verified signature90b9dd3
-
vision-fp16 snippet: drop Marqo dependency, use vanilla ViT-B-16-SigLIP architecture (186 MB true footprint)
Verified signature33d687a
-
Fix Related Models self-link: replace broken relative (.) with bold (no link)
Verified signaturee300816 -
Related Models: include all 5 checkpoints (was missing vision-fp16); uniform table across cards
Verified signature6b92fb7 -
Update tags (remove clip/open_clip; unify distillation); change license to MIT
Verified signature88d9e52 -
Replace @misc paper-style citation with @software repo citation
Verified signature825c308 -
Fix citation URL: github.com/srpone/MODA -> github.com/hopit-ai/Moda; standardize across all model cards
Verified signature1fd7eb3 -
Upload folder using huggingface_hub
Verified signature7df9bf8 -
initial commit
Verified signaturea176f42