Image Feature Extraction
timm
English
levljepa
vision-language
self-supervised-learning
feature-extraction
non-contrastive
jepa
vit
gpt2
Instructions to use lukaskuhn/LeVLJEPA-ViT-B-DataComp-200k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use lukaskuhn/LeVLJEPA-ViT-B-DataComp-200k with timm:
import timm model = timm.create_model("hf_hub:lukaskuhn/LeVLJEPA-ViT-B-DataComp-200k", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "levljepa", | |
| "model_size": "base", | |
| "vision_encoder": { | |
| "architecture": "vit_base_patch16_224", | |
| "hidden_size": 768, | |
| "num_layers": 12, | |
| "num_heads": 12 | |
| }, | |
| "text_encoder": { | |
| "architecture": "gpt2", | |
| "hidden_size": 768, | |
| "num_layers": 12, | |
| "num_heads": 12, | |
| "vocab_size": 50257 | |
| }, | |
| "embed_dim": 256, | |
| "projector_width": 2048, | |
| "projector_depth": 4, | |
| "training": { | |
| "dataset": "DataComp-large", | |
| "total_steps": 200000, | |
| "batch_size": 4096 | |
| } | |
| } |