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:
- b85dbff7b705fcf2aa50afe95b821f3f5dfd2539dbc58bdd8c459df45647a895
- Size of remote file:
- 84.4 MB
- SHA256:
- 412d987ab5dbb9751164d755f9bcb003fd743bd2d3f401a7561bb42ff42b00e3
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