mx-maskrcnn-master
说明： 我们提出了一个简单、灵活和通用的对象实例分割框架。我们的方法能有效检测图像中的对象，同时为每个实例生成高质量的 segmentation mask。这种被称为 Mask R-CNN 的方法通过添加用于预测 object mask 的分支来扩展 Faster R-CNN，该分支与用于边界框识别的现有分支并行。Mask R-CNN 训练简单，只需在以 5fps 运行的 Faster R-CNN 之上增加一个较小的 overhead。此外，Mask R-CNN 很容易推广到其他任务，例如它可以允许同一个框架中进行姿态估计。我们在 COCO 系列挑战的三个轨道任务中均取得了最佳成果，包括实例分割、边界对象检测和人关键点检测。没有任何 tricks，Mask R-CNN 的表现优于所有现有的单一模型取得的成绩，包括 COCO 2016 挑战赛的冠军。
(Mask R-CNN code by HeKaiming)
(Mask R-CNN code by HeKaiming)
文件列表:
LICENSE (11357, 2018-02-28)
Makefile (221, 2018-02-28)
data (0, 2018-02-28)
data\cityscape (0, 2018-02-28)
data\cityscape\imglists (0, 2018-02-28)
data\cityscape\imglists\test.lst (200205, 2018-02-28)
data\cityscape\imglists\train.lst (412545, 2018-02-28)
data\cityscape\imglists\val.lst (67790, 2018-02-28)
demo_mask.py (2115, 2018-02-28)
eval_maskrcnn.py (2113, 2018-02-28)
figures (0, 2018-02-28)
figures\maskrcnn_result.png (900697, 2018-02-28)
figures\test.jpg (40967, 2018-02-28)
incubator-mxnet (0, 2018-02-28)
rcnn (0, 2018-02-28)
rcnn\CXX_OP (0, 2018-02-28)
rcnn\CXX_OP\roi_align-inl.h (8596, 2018-02-28)
rcnn\CXX_OP\roi_align.cc (2824, 2018-02-28)
rcnn\CXX_OP\roi_align.cu (12308, 2018-02-28)
rcnn\CXX_OP\roi_align_v1-inl.h (15877, 2018-02-28)
rcnn\CXX_OP\roi_align_v1.cc (3090, 2018-02-28)
rcnn\CXX_OP\roi_align_v1.cu (446, 2018-02-28)
rcnn\PY_OP (0, 2018-02-28)
rcnn\PY_OP\__init__.py (0, 2018-02-28)
rcnn\PY_OP\fpn_roi_pooling.py (4584, 2018-02-28)
rcnn\PY_OP\mask_output.py (1971, 2018-02-28)
rcnn\PY_OP\mask_roi.py (2240, 2018-02-28)
rcnn\PY_OP\proposal_fpn.py (8149, 2018-02-28)
rcnn\__init__.py (0, 2018-02-28)
rcnn\config.py (5104, 2018-02-28)
rcnn\core (0, 2018-02-28)
rcnn\core\__init__.py (0, 2018-02-28)
rcnn\core\callback.py (1710, 2018-02-28)
rcnn\core\loader.py (24515, 2018-02-28)
rcnn\core\metric.py (9044, 2018-02-28)
rcnn\core\module.py (8588, 2018-02-28)
... ...
Makefile (221, 2018-02-28)
data (0, 2018-02-28)
data\cityscape (0, 2018-02-28)
data\cityscape\imglists (0, 2018-02-28)
data\cityscape\imglists\test.lst (200205, 2018-02-28)
data\cityscape\imglists\train.lst (412545, 2018-02-28)
data\cityscape\imglists\val.lst (67790, 2018-02-28)
demo_mask.py (2115, 2018-02-28)
eval_maskrcnn.py (2113, 2018-02-28)
figures (0, 2018-02-28)
figures\maskrcnn_result.png (900697, 2018-02-28)
figures\test.jpg (40967, 2018-02-28)
incubator-mxnet (0, 2018-02-28)
rcnn (0, 2018-02-28)
rcnn\CXX_OP (0, 2018-02-28)
rcnn\CXX_OP\roi_align-inl.h (8596, 2018-02-28)
rcnn\CXX_OP\roi_align.cc (2824, 2018-02-28)
rcnn\CXX_OP\roi_align.cu (12308, 2018-02-28)
rcnn\CXX_OP\roi_align_v1-inl.h (15877, 2018-02-28)
rcnn\CXX_OP\roi_align_v1.cc (3090, 2018-02-28)
rcnn\CXX_OP\roi_align_v1.cu (446, 2018-02-28)
rcnn\PY_OP (0, 2018-02-28)
rcnn\PY_OP\__init__.py (0, 2018-02-28)
rcnn\PY_OP\fpn_roi_pooling.py (4584, 2018-02-28)
rcnn\PY_OP\mask_output.py (1971, 2018-02-28)
rcnn\PY_OP\mask_roi.py (2240, 2018-02-28)
rcnn\PY_OP\proposal_fpn.py (8149, 2018-02-28)
rcnn\__init__.py (0, 2018-02-28)
rcnn\config.py (5104, 2018-02-28)
rcnn\core (0, 2018-02-28)
rcnn\core\__init__.py (0, 2018-02-28)
rcnn\core\callback.py (1710, 2018-02-28)
rcnn\core\loader.py (24515, 2018-02-28)
rcnn\core\metric.py (9044, 2018-02-28)
rcnn\core\module.py (8588, 2018-02-28)
... ...
# MX Mask R-CNN
An MXNet implementation of [Mask R-CNN](https://arxiv.org/abs/1703.06870).
This repository is based largely on the mx-rcnn implementation of Faster RCNN available [here](https://github.com/precedenceguo/mx-rcnn).
## Main Results
### Cityscapes
| Method |Training data| Test data| Average | person | rider | car | truck | bus | train| motorcycle| bicycle|
|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| Ours| fine-only |test|26.9|33.0|25.7|47.7|21.6|27.4|23.0|19.9|16.9|
| Reference[5]| fine-only |test|26.2|30.5|23.8|46.9|22.8|32.2|18.6|19.1|16.0|
| Ours | fine-only |val|31.3|32.6|26.6|49.5|26.5|45.4|32.1|17.6|20.4|
| Reference[5]| fine-only |val|31.5| -| -| -| -| -| -| -| -| -| -|
- Backbone: Resnet-50-FPN
### COCO
Coming soon, please stay tuned.
## Requirement
We tested our code on:
Ubuntu 16.04, Python 2.7 with
numpy(1.12.1), cv2(2.4.9), PIL(4.3), matplotlib(2.1.0), cython(0.26.1), easydict
## Preparation for Training
1. Download Cityscapes data (gtFine_trainvaltest.zip, leftImg8bit_trainvaltest.zip). Extract them into 'data/cityscape/'.
The folder structure would then look as shown below:
```
data/cityscape/
├── leftImg8bit/
│ ├── train/
│ ├── val/
│ └── test/
├── gtFine/
│ ├── train/
│ ├── val/
│ └── test/
└── imglists/
├── train.lst
├── val.lst
└── test.lst
```
2. Download Resnet-50 pretrained model.
```
bash scripts/download_res50.sh
```
3. Build MXNet with ROIAlign operator.
```
cp rcnn/CXX_OP/* incubator-mxnet/src/operator/
```
To build MXNet from source, please refer to the [tutorial](https://mxnet.incubator.apache.org/get_started/build_from_source.html).
4. Build related cython code.
```
make
```
5. Kick off training
```
bash scripts/train_alternate.sh
```
## Preparation for Evaluation
1. Prepare Cityscapes evaluation scripts.
```
bash scripts/download_cityscapescripts.sh
```
2. Eval
```
bash scripts/eval.sh
```
## Demo
1. Download model, available at [Dropbox](https://www.dropbox.com/s/zidcbbt7apwg3z6/final-0000.params?dl=0)/[BaiduYun](https://pan.baidu.com/s/1o8n4VMU), and place it in the model folder.
2. Make sure that you have the cityscapes data in 'data/cityscapes' folder.
```
bash scripts/demo.sh
```
## Test single image
1. Download model, available at [Dropbox](https://www.dropbox.com/s/zidcbbt7apwg3z6/final-0000.params?dl=0)/[BaiduYun](https://pan.baidu.com/s/1o8n4VMU), and place it in the model folder.
2. Follow `Preparation for Training` (step1-step4)
3. run `bash scripts/demo_single_image.sh`, you can change the image path in script demo_single_image.sh.
## FAQ
Q: It says **`AttributeError: 'module' object has no attribute 'ROIAlign'`**.
A: This is because either
- you forget to copy the operators to your MXNet folder
- or you forget to re-compile MXNet and re-install MXNet python interface
- or you install the wrong MXNet
Please print `mxnet.__path__` to make sure you use correct MXNet
Q: I encounter **`incubator-mxnet/mshadow/mshadow/././././cuda/tensor_gpu-inl.cuh:110: Check failed: err == cudaSuccess (7 vs. 0) Name: MapPlanKernel ErrStr:too many resources requested for launch`** at the begining.
A: Please try adding `MSHADOW_CFLAGS += -DMSHADOW_OLD_CUDA=1` in `mxnet/mshadow/make/mshadow.mk` and re-compile MXNet.
## References
1. Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. In Neural Information Processing Systems, Workshop on Machine Learning Systems, 2015
2. Ross Girshick. "Fast R-CNN." In Proceedings of the IEEE International Conference on Computer Vision, 2015.
3. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016.
4. Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie. "Feature Pyramid Networks for Object Detection." In Computer Vision and Pattern Recognition, IEEE Conference on, 2017.
5. Kaiming He, Georgia Gkioxari, Piotr Dollar, Ross Girshick. "Mask R-CNN." In Proceedings of the IEEE International Conference on Computer Vision, 2017.
4. Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. "Caffe: Convolutional architecture for fast feature embedding." In Proceedings of the ACM International Conference on Multimedia, 2014.
5. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. "ImageNet: A large-scale hierarchical image database." In Computer Vision and Pattern Recognition, IEEE Conference on, 2009.
6. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition". In Computer Vision and Pattern Recognition, IEEE Conference on, 2016.
7. Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, Bernt Schiele. "The Cityscapes Dataset for Semantic Urban Scene Understanding." In Computer Vision and Pattern Recognition, IEEE Conference on, 2016.
近期下载者：
相关文件：
收藏者：