LibreVJEPA2l256-cls-diving48

LibreVJEPA2l256-cls-diving48

V-JEPA 2.0 l256 attentive probe, diving48 video classification, converted for LibreYOLO.

from libreyolo import LibreYOLO

model = LibreYOLO("LibreVJEPA2l256-cls-diving48.pt")
result = model.predict("clip.mp4")

Source

Converted from facebook/vjepa2-vitl-fpc32-256-diving48 at revision 71ae2a8b1ff5a297aeeaae9b5e64c7a2e5e6a633.

Modifications

The upstream checkpoint is remapped into LibreYOLO's native V-JEPA 2 module and wrapped with LibreYOLO v1.0 checkpoint metadata (family, size, task, dataset variant, clip geometry, preprocessing and the pooling rule). Tensor values are unchanged: the conversion is a key remap, loaded strictly, and the self-supervised predictor tower is dropped as a named, asserted set rather than by substring match.

Parity against unmodified transformers==5.1.0 on float32 CPU is exact (max_abs_diff == 0.0) for single-view logits.

Embedding contract

This probe applies the exact upstream three-layer attentive pooler and linear classifier. One temporal view is used by default; the published multi-view accuracy is NOT claimed from single-view inference.

V-JEPA 2 is trained on video. An image is accepted as a single-frame input, which is a static appearance representation, not a motion one.

License

These weights are MIT, inherited from the source checkpoint above. The full text is in LICENSE, with attribution in NOTICE. Licences in this family differ per artifact, so do not assume a family-wide licence.

The probe was trained on a third-party video dataset, named for provenance only. Its dataset terms are not the terms of these weights, and LibreYOLO does not mirror or auto-download it.