Instructions to use apple/DFN2B-CLIP-ViT-L-14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use apple/DFN2B-CLIP-ViT-L-14 with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:apple/DFN2B-CLIP-ViT-L-14') tokenizer = open_clip.get_tokenizer('hf-hub:apple/DFN2B-CLIP-ViT-L-14') - Notebooks
- Google Colab
- Kaggle
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README.md
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A CLIP (Contrastive Language-Image Pre-training) model trained on DFN-2B.
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Data Filtering Networks (DFNs) are small used to automatically filter large pools of uncurated data.
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This model was trained on 2B images that were filtered from a pool of 12.8B uncurated image-text pairs
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(12.8B image-text pairs from CommonPool-12.8B).
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---
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A CLIP (Contrastive Language-Image Pre-training) model trained on DFN-2B.
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Data Filtering Networks (DFNs) are small networks used to automatically filter large pools of uncurated data.
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This model was trained on 2B images that were filtered from a pool of 12.8B uncurated image-text pairs
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(12.8B image-text pairs from CommonPool-12.8B).
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