---
library_name: lucid
license: bsd-3-clause
tags:
  - instance-segmentation
  - mask
  - lucid
datasets:
  - coco
pipeline_tag: image-segmentation
model-index:
  - name: mask-rcnn-resnet50-fpn
    results:
      - task: { type: instance-segmentation }
        dataset: { name: COCO, type: coco }
        metrics:
          - { type: box mAP, value: 37.9 }
          - { type: mask mAP, value: 34.6 }
---

# Mask R-CNN (ResNet-50-FPN)

> He et al., 2017 — *Mask R-CNN* (arXiv:1703.06870)

[Lucid](https://github.com/ChanLumerico/lucid) port of `torchvision/MaskRCNN_ResNet50_FPN_Weights.COCO_V1`,
converted to Lucid-native safetensors.

## Available weights

| Tag | box mAP | mask mAP | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
| `COCO_V1` *(default)* | 37.9 | 34.6 | 44.4M | 134.38 | 169.81 MB | torchvision |

## Usage

```python
import lucid.models as models
from lucid.models.weights import MaskRCNNResNet50FPNWeights

# default tag
model = models.mask_rcnn_resnet50_fpn(pretrained=True)

# explicit tag (enum or string)
model = models.mask_rcnn_resnet50_fpn(weights=MaskRCNNResNet50FPNWeights.COCO_V1)
model = models.mask_rcnn_resnet50_fpn(pretrained="COCO_V1")

# preprocessing travels with the weights
weights = MaskRCNNResNet50FPNWeights.COCO_V1
preprocess = weights.transforms()
out = model(preprocess(image)[None])
# InstanceSegmentationOutput: class logits + boxes + per-instance masks
logits, boxes, masks = out.logits, out.pred_boxes, out.pred_masks
```

## Conversion

Converted from `torchvision/MaskRCNN_ResNet50_FPN_Weights.COCO_V1` via
`python -m tools.convert_weights mask_rcnn_resnet50_fpn --tag COCO_V1`.
Key mapping + numerical parity verified against the source.

## License

`bsd-3-clause` — inherited from the original weights.

## Citation

```
@inproceedings{he2017mask,
  title={Mask R-CNN},
  author={He, Kaiming and Gkioxari, Georgia and Doll{\'a}r, Piotr and Girshick, Ross},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
  pages={2961--2969},
  year={2017}
}
```