---
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
---

# 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.

<!-- gen-cards:use-it begin id=vjepa2-vitl-ssv2 (managed by scripts/gen-cards — edit cards.json / QuickStart.swift, not this block) -->
## Use it

▶️ **Run it (source)** — the [ActionCamera runner](https://github.com/john-rocky/coreai-kit/tree/main/Examples/ActionCamera)
(live camera action recognition, one app for every video model in the catalog):

```bash
git clone https://github.com/john-rocky/coreai-kit
open coreai-kit/Examples/ActionCamera/ActionCamera.xcodeproj
# → Run, then pick "V-JEPA 2 ViT-L (SSv2)" in the model picker

# agents / headless (macOS):
cd coreai-kit/Examples/ActionCamera
swift run action-cli --model vjepa2-vitl-ssv2 --video sample.mp4
```

💻 **Build with it** — complete; the glue is kit API, copy-paste runs:

```swift
import CoreAIKitVision

let recognizer = try await ActionRecognizer(catalog: "vjepa2-vitl-ssv2")
let actions = try await recognizer.classify(videoAt: videoURL, topK: 3)
// actions: ranked [Prediction] — .label ("Pushing [something] from left to right"),
// .probability; 174 SSv2 classes, fully on-device
```

The take-home is [`Examples/ActionCamera/Sources/QuickStart.swift`](https://github.com/john-rocky/coreai-kit/blob/main/Examples/ActionCamera/Sources/QuickStart.swift)
— this exact code as one typed function, no UI; the CLI is an argument shell over it, and
the GUI classifies a rolling 16-frame clip from `CameraFeed`.
Live camera? Keep the last 16 `CameraFeed` frames and call `classify(frames:)` — other
frame counts are uniformly resampled to 16. The bundled `sample.mp4` is a synthetic
clip (a hand pushing a block); point `--video` at real footage for real results.

**Integration checklist**

- SPM: `https://github.com/john-rocky/coreai-kit` → product **CoreAIKitVision**
- Info.plist: `NSCameraUsageDescription` — only for the live camera; the snippet needs none
- Entitlements: none needed
- First run downloads the model — 0.7 GB (Mac) / 1.4 GB (iPhone) — then it loads from the
  local cache (Application Support; progress via the `downloadProgress` callback)
- Measure in Release — Debug is ~3× slower on per-token host work
<!-- gen-cards:use-it end -->

## 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).