Mask_RCNN_Pytorch

This is an implementation of the instance segmentation model Mask R-CNN on Pytorch, based on the previous work of Matterport and lasseha. Matterport's repository is an implementation on Keras and TensorFlow while lasseha's repository is an implementation on Pytorch.

Features

Compared with other PyTorch implementations, this repository has the following features:

It supports multi-image batch training (i.e., batch size >1).
It supports PyTorch 0.4.0 (Currently does not support Pytorch >1.0).
It supports both GPU and CPU. You can use a CPU to visualize the results.
It supports multiple GPUs training (please look at instrctions here).
You could train Mask R-CNN on your own dataset (please see synthia.py, which demonstrates how we trained a model on Synthia Dataset, starting from the model pre-trained on COCO Dataset).
You could use a model pre-trained on COCO or ImageNet to segment objects in your own images (please see demo_coco.py or demo_synthia.py).

Requirements

Python 3
Linux
PyTorch 0.4.0
matplotlib, scipy, skimage, h5py, numpy

Demo

Synthia Dataset

COCO dataset

Compilation

The instructions come from lasseha's repository.

We use the Non-Maximum Suppression from ruotianluo and the RoiAlign from longcw. Please follow the instructions below to build the functions.

  cd nms/src/cuda/
  nvcc -c -o nms_kernel.cu.o nms_kernel.cu -x cu -Xcompiler -fPIC -arch=arch
  cd ../../
  python build.py
  cd ../

  cd roialign/roi_align/src/cuda/
  nvcc -c -o crop_and_resize_kernel.cu.o crop_and_resize_kernel.cu -x cu -Xcompiler -fPIC -arch=arch
  cd ../../
  python build.py
  cd ../../

where 'arch' is determined by your GPU model:

GPU	TitanX	GTX 960M	GTX 1070	GTX 1080 (Ti)
arch	sm_52	sm_50	sm_61	sm_61

If you want to train the network on the COCO dataset, please install the Python COCO API and create a symlink.
```
  ln -s /path/to/coco/cocoapi/PythonAPI/pycocotools/ pycocotools
```
The pretrained models on COCO and ImageNet are available here.

Results(COCO)

The training and evaluation is based on COCO Dataset 2014. To understand the indicators below, please have a look at pycocotools. Notably, I only used one GTX 1080 (Ti). I think the performance could be improved if more GPUs are available.

Indicator	IoU	area	maxDets	Value
Average Precision (AP)	0.50:0.95	all	100	0.392
Average Precision (AP)	0.50	all	100	0.574
Average Precision (AP)	0.75	all	100	0.434
Average Precision (AP)	0.50:0.95	small	100	0.199
Average Precision (AP)	0.50:0.95	medium	100	0.448
Average Precision (AP)	0.50:0.95	large	100	0.575
Average Recall (AR)	0.50:0.95	all	1	0.321
Average Recall (AR)	0.50:0.95	all	10	0.445
Average Recall (AR)	0.50:0.95	all	100	0.457
Average Recall (AR)	0.50:0.95	small	100	0.231
Average Recall (AR)	0.50:0.95	medium	100	0.508
Average Recall (AR)	0.50:0.95	large	100	0.645

cinly0/Mask_RCNN_Pytorch