---
license: bsd-3-clause
library_name: libreyolo
pipeline_tag: object-detection
datasets:
  - detection-datasets/coco
tags:
  - object-detection
  - fcos
  - torchvision
  - libreyolo
---

# LibreFCOSr50

FCOS with a ResNet-50 FPN backbone, repackaged for LibreYOLO.

```python
from libreyolo import LibreYOLO

model = LibreYOLO("LibreFCOSr50.pt")
results = model.predict("image.jpg")
```

## Source

Derived from [pytorch/vision](https://github.com/pytorch/vision) at commit
[`336d36e8db990a905498c73933e35231876e28bc`](https://github.com/pytorch/vision/commit/336d36e8db990a905498c73933e35231876e28bc).
Copyright (c) Soumith Chintala 2016 and torchvision contributors. The source
implementation is BSD-3-Clause.

Official checkpoint: [fcos_resnet50_fpn_coco-99b0c9b7.pth](https://download.pytorch.org/models/fcos_resnet50_fpn_coco-99b0c9b7.pth)
SHA-256: `99b0c9b7cfb1527d782db86b91d207f00547c792fb4103fc612b651d0a07b9e7`
Published COCO val2017 box mAP: 39.2.

## Modifications

Checkpoint metadata was added for LibreYOLO's v1.0 schema. Learned tensors and
state-dict keys are unchanged. The native LibreYOLO graph loads all 319
official state entries strictly and matches the pinned source at raw heads,
anchors, preprocessing, and final detections. See
`weights/convert_fcos_weights.py` in the
[LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo).

## Benchmarks

Independent accuracy and speed benchmarks:
[visionanalysis.org/model/fcos-r50](https://www.visionanalysis.org/model/fcos-r50)

## License

The checkpoint publisher did not attach a separate per-object license file.
This mirror applies the releasing project's BSD-3-Clause license on an
**implied**, not publisher-confirmed, basis. Torchvision warns that pretrained
models may have their own licenses or terms derived from training data and
that users must determine whether they have permission for their use case.
COCO annotations are CC BY 4.0; source images retain their individual Flickr
terms. See [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE).