Feature Extraction
Transformers
PyTorch
vjepa2_fmri_encoder_enhanced
neuroscience
fmri
video
v-jepa
brain-alignment
custom_code
Instructions to use epfl-neuroai/vjepa2-encoder-enhanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use epfl-neuroai/vjepa2-encoder-enhanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="epfl-neuroai/vjepa2-encoder-enhanced", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("epfl-neuroai/vjepa2-encoder-enhanced", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b43931ea359f9c246e9b1c6546c05d84e88373335bc16ea7b91cbeb1dc58865f
- Size of remote file:
- 84.4 MB
- SHA256:
- f1837f67db8ace6781126370be817d992696fe73cf4969eae75744849ed84d7c
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