| --- |
| license: mit |
| base_model: facebook/vjepa2-vitl-fpc16-256-ssv2 |
| tags: |
| - coreai |
| - apple |
| - video-classification |
| - v-jepa |
| - world-model |
| - on-device |
| pipeline_tag: video-classification |
| --- |
| |
| > **Mirror** of [`mlboydaisuke/VJEPA2-ViTL-SSv2-CoreAI`](https://huggingface.co/mlboydaisuke/VJEPA2-ViTL-SSv2-CoreAI) — the canonical repo ([CoreAI Model Zoo](https://github.com/john-rocky/coreai-model-zoo)). Updates land there first. |
|
|
|
|
| # V-JEPA 2 (ViT-L, SSv2 action recognition) — Apple Core AI |
|
|
| [V-JEPA 2](https://huggingface.co/facebook/vjepa2-vitl-fpc16-256-ssv2) (Meta AI) running natively on |
| the Apple Core AI engine — the zoo's first **world model**: a self-supervised video encoder that |
| learns by predicting in representation space (JEPA), here with the Something-Something v2 action |
| head (174 classes of *physical interactions* — put/lift/push/roll/cover/pretend…). |
|
|
| - **One bundle**: ViT-L backbone (3D RoPE attention) + attentive pooler + classifier, ~375M params, |
| fp16 ~675 MB. |
| - **I/O**: `pixel_values_videos [1,16,3,256,256]` (16 frames, RGB 0..1, ImageNet mean/std) → |
| `logits [1,174]` (`labels.json`). |
| - **Verified**: engine vs PyTorch reference cosine 0.999996, top-5 identical; a synthetic |
| motion probe (square moving up vs down) flips the predicted direction correctly. |
| - **Speed**: ~150–180 ms per 16-frame clip on an M4 Max (GPU) — real-time video understanding. |
|
|
| ## Files |
|
|
| | path | what | |
| |---|---| |
| | `macos/vjepa2_ssv2_fp16.aimodel` | fp16 bundle (macOS / JIT) | |
| | `ios/vjepa2_ssv2_fp16.h18p.aimodelc` | iOS AOT bundle (iPhone, A18 Pro+ GPU) | |
| | `macos/labels.json`, `ios/labels.json` | 174 SSv2 class names | |
| | `macos/metadata.json` | I/O + preprocessing spec | |
|
|
| **Live demo app**: [coreai-video](https://github.com/john-rocky/coreai-model-zoo/tree/main/apps/coreai-video) |
| — camera → live top-3 actions. iPhone 17 Pro: ~0.34 s per 16-frame clip. |
|
|
| ## Preprocessing |
|
|
| Sample 16 frames uniformly from the clip, resize+center-crop to 256×256, scale to 0..1, normalize |
| with ImageNet mean `[0.485,0.456,0.406]` / std `[0.229,0.224,0.225]`, layout `[1,16,3,256,256]`. |
|
|
| ## Credits |
|
|
| - **Meta AI** — [V-JEPA 2](https://github.com/facebookresearch/vjepa2) (MIT). |
| - Conversion + Core AI port: [coreai-model-zoo](https://github.com/john-rocky/coreai-model-zoo). |
|
|
