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Feature Pyramid Networks for Object Detection

This is an implementation of FPN (Feature Pyramid Networks) using Chainer

Performance

mmAP on COCO 2014 minival

Requirements

• Python 3.6
• Chainer 4.0+
• CuPy 4.0+
• ChainerCV (we need to build from master branch)
• ChainerMN 1.3
• pycocotools

Demo

$ curl -LO https://github.com/Hakuyume/chainer-fpn/releases/download/assets/faster_rcnn_fpn_resnet50_coco.npz
$ python3 demo.py [--gpu <gpu>] --model resnet50 --pretrained-model faster_rcnn_fpn_resnet50_coco.npz <image>

Training

$ mpiexec -n <#gpu> python3 train_coco.py --model resnet50

Our experiments were conducted with 8 GPUs.

Evaluation

$ python3 eval_coco.py [--gpu <gpu>] --model resnet50 --pretrained-model faster_rcnn_fpn_resnet50_coco.npz

or

$ python3 eval_coco.py [--gpu <gpu>] --model resnet50 --snapshot result/snapshot_iter_90000

Convert weights from Detectron

1. Download weights from Detectron's model zoo.

$ curl -L https://s3-us-west-2.amazonaws.com/detectron/35857345/12_2017_baselines/e2e_faster_rcnn_R-50-FPN_1x.yaml.01_36_30.cUF7QR7I/output/train/coco_2014_train%3Acoco_2014_valminusminival/generalized_rcnn/model_final.pkl -o e2e_faster_rcnn_R-50-FPN_1x.pkl
$ curl -L https://s3-us-west-2.amazonaws.com/detectron/35857890/12_2017_baselines/e2e_faster_rcnn_R-101-FPN_1x.yaml.01_38_50.sNxI7sX7/output/train/coco_2014_train%3Acoco_2014_valminusminival/generalized_rcnn/model_final.pkl -o e2e_faster_rcnn_R-101-FPN_1x.pkl

2. Convert weights.

$ python3 detectron2npz.py e2e_faster_rcnn_R-50-FPN_1x.pkl faster_rcnn_fpn_resnet50_coco.npz

Note: Since the mean value in Detectron is different from that in ChainerCV, --mean=detectron option should be specified for converted weights.

$ python3 eval_coco.py [--gpu <gpu>] --model resnet50 --mean=detectron --pretrained-model faster_rcnn_fpn_resnet50_coco.npz

References

1. Tsung-Yi Lin et al. "Feature Pyramid Networks for Object Detection" CVPR 2017
2. Detectron
3. Mask R-CNN by @wkentaro (for the implementation of RoIAlign)