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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b99077f907a18f6790d6585c3ad9be2c9ad07ea1d498226757c305947785773
- Size of remote file:
- 84.4 MB
- SHA256:
- 9a7f389742be6fca1a30254c7b3de75c0f3e332290ddd7b5644f4f114d8a80a6
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