---
license: mit
library_name: libreyolo
tags:
  - libreyolo
  - reid
  - person-reidentification
  - tracking
  - osnet
---

# LibreReID-osnet

OSNet-AIN person re-identification weights for LibreYOLO's Deep OC-SORT tracker (`model.track(source, tracker="deepocsort")`). Downloaded automatically on first use to `~/.cache/libreyolo/reid/`.

## Files

| File | Size | SHA-256 |
|---|---|---|
| `osnet_ain_x0_25.pt` | 1.0 MB | `ce171fe160b3608f5e4c19489774991419be965b1d6f4bdccc4b4cfd2ef95347` |
| `osnet_ain_x0_5.pt` | 2.7 MB | `510bcebae21bd0c0fcc7df388e97d2f687a9ee4befa4394d6fb1fb19aac0bce2` |
| `osnet_ain_x0_75.pt` | 5.4 MB | `57b31d7f806edac586540e08e98c589f0010ad7876dacf4af013deaa284dd26d` |
| `osnet_ain_x1_0.pt` | 8.9 MB | `34c24e98b6b70c8b62480f846fd0d581aa2fd1535bc0276aecf1f10430b731d1` |

`osnet_ain_x0_25` is the LibreYOLO default. All files are plain PyTorch state dicts producing L2-normalized 512-d embeddings.

## Provenance and license

- Network: OSNet-AIN, ported to LibreYOLO from [Torchreid](https://github.com/KaiyangZhou/deep-person-reid) (MIT). The LibreYOLO port is state-dict compatible and bit-exact against upstream (`tests/unit/test_reid.py`).
- Weights: converted unchanged from the Torchreid model zoo multi-source (MS+D+C) OSNet-AIN checkpoints, released under the repository's MIT license. Conversion script: `weights/convert_osnet_reid_weights.py` in the LibreYOLO repository (strips the classifier head, keeps feature layers, verifies strict load).
- Training data: the upstream checkpoints were trained by the Torchreid authors on person re-identification research datasets (MSMT17, DukeMTMC-reID, CUHK03). Those datasets carry research-oriented terms; the weights themselves are distributed under MIT by the upstream author. Review your own use case if you deploy person re-identification in production.

## Usage

```python
from libreyolo import LibreYOLO

model = LibreYOLO("LibreYOLO9t.pt")
for result in model.track("video.mp4", tracker="deepocsort"):
    print(result.track_id)
```