MaskRCNN

Mask R-CNN is for "instance segmentation". Please reference https://arxiv.org/abs/1703.06870.

Example 1

Example2

Demo

python predict.py images/car58a54312d.jpg

Training

1. Put Coco files under data directory.

data/
├── annotations
├── test2014
├── train2014
└── val2014

2. ./train.sh

Evaluation

./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

Requirements

• Python 3.6.2

• Pytorch 1.0.0

• matplotlib, scipy, scikit-image

pip install scipy==1.2.1

Installation

1. Clone this repository.

    git clone https://github.com/delldu/MaskRCNN.git
   

2. Download pre-trained model.

Download mask_rcnn_coco.pth from https://pan.baidu.com/s/1HVUdfrFKPMGlMcUP7mXZGw

and put it under models .

3. Install c++ extension packages

   cd c++ext make cd ../
   cd cocoapi/PythonAPI make cd ../..
   

Thanks

1. Mask R-CNN https://arxiv.org/abs/1703.06870

2. https://github.com/multimodallearning/pytorch-mask-rcnn

Chinese Document

中文文档