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README.md

Example codes for FCIS [1]

Performance

SBD Train & Test

Model mAP@0.5 (Original [1]) mAP@0.7 (Original [1]) mAP@0.5 (weight conversion) mAP@0.7 (weight conversion) mAP@0.5 (train) mAP@0.7 (train)
FCIS ResNet101 65.7 52.1 64.2 51.2 64.1 (1 GPU) 51.2 (1 GPU)

Demo

Segment objects in an given image. This demo downloads SBD pretrained model automatically if a pretrained model path is not given.

python demo.py [--gpu <gpu>] [--pretrained-model <model_path>] <image.jpg>

Evaluation

The evaluation can be conducted using chainercv/examples/instance_segmentation/eval_sbd.py

Train

You can train the model with the following code. Note that this code requires SciPy module.

python train.py [--gpu <gpu>]

If you want to use multiple GPUs, use train_multi.py. Note that this code requires chainermn module.

mpi4exec -n <n_gpu> python train_multi.py --lr  <n_gpu>*0.0005

You can download weights that were trained by ChainerCV.

Convert Mxnet model

Convert *.params to *.npz. Note that the number of classes and network structure is specified by --dataset.

python mxnet2npz.py [--dataset sbd|coco] [--out <npz filename>] <param filename>

References

  1. Yi Li et al. "Fully Convolutional Instance-aware Semantic Segmentation" CVPR 2017.