---
license: bsd-3-clause
library_name: libreyolo
pipeline_tag: image-segmentation
datasets:
  - detection-datasets/coco
tags:
  - instance-segmentation
  - object-detection
  - mask-rcnn
  - torchvision
  - libreyolo
---

# LibreMaskRCNNr50

Mask R-CNN with a ResNet-50-FPN v2 backbone, repackaged for LibreYOLO. The
checkpoint supports instance segmentation by default and box-only detection
with `task="detect"`.

```python
from libreyolo import LibreYOLO

model = LibreYOLO("LibreMaskRCNNr50.pt")
result = model.predict("image.jpg")
print(result.boxes.xyxy, result.masks.data)
```

## 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: [maskrcnn_resnet50_fpn_v2_coco-73cbd019.pth](https://download.pytorch.org/models/maskrcnn_resnet50_fpn_v2_coco-73cbd019.pth)

- Official SHA-256: `73cbd0190fcbe3ba339921fbce2c3a0b6bb9126c9a133c85e43a2a8e060a109e`
- Converted SHA-256: `9214933a07cd354265e62c31298d4502f8433d124da6fea1b3c00cf78974cfbd`
- Published COCO val2017 box mAP: 47.4
- Published COCO val2017 mask mAP: 41.8

## Modifications

LibreYOLO checkpoint metadata was added. Learned tensors and state-dict keys
are unchanged. The native graph loads the official state dict strictly and has
exact eager parity at the RPN head, box head, final boxes, raw mask logits, and
full-image masks. The batch-1 opset-18 ONNX graph is also covered by ONNX
Runtime parity. See `weights/convert_mask_rcnn_weights.py` in the
[LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo).

## 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).