Mask R-CNN is for "instance segmentation". Please reference https://arxiv.org/abs/1703.06870.
Example 1
Example2
python predict.py images/car58a54312d.jpg
1. Put Coco files under data directory.
data/
├── annotations
├── test2014
├── train2014
└── val2014
2. ./train.sh
./eval.sh
DONE (t=2.57s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.317
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.525
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.336
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.139
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.366
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.492
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.261
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.369
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.379
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.169
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.425
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.562
Prediction time: 349.81333112716675. Average 0.6996266622543335/image
Total time: 401.12283730506897
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Python 3.6.2
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Pytorch 1.0.0
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matplotlib, scipy, scikit-image
pip install scipy==1.2.1
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Clone this repository.
git clone https://github.com/delldu/MaskRCNN.git -
Download pre-trained model.
Download mask_rcnn_coco.pth from https://pan.baidu.com/s/1HVUdfrFKPMGlMcUP7mXZGw
and put it under models .
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Install c++ extension packages
cd c++ext make cd ../ cd cocoapi/PythonAPI make cd ../..