MaskRCNN_Tensorflow_Docker

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MaskRCNN_Tensorflow_Docker

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MaskRCNN + Tensorflow + Docker

// repository documentation

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# MaskRCNN_Tensorflow_Docker

Integration of [FastMaskRCNN] + [Tensorflow] + [Nvidia-docker]. (Tested in Ubuntu 16.04)

- Creates docker image "user/tensorflow_gpu_mrcnn" using [Tensorflow_gpu] + [FastMaskRCNN] dependencies
  - Creates docker container "user-mrcnn_tf1.1" from "user/tensorflow_gpu_mrcnn"
  - Clones [FastMaskRCNN] code inside MaskRCNN_Tensorflow_Docker/MRCNN/ (host)
  - Mounts MRCNN/ (host) at /home/user/ (docker) inside "user-mrcnn_tf1.1" container.
    (Host and Docker are now sharing the same code i.e. FastMaskRCNN. So code changes can be made at host side and code can be run inside the docker container)
  - Downloads/Copies datasets required for [FastMaskRCNN] inside FastMaskRCNN (visible at both host and docker due to mounting)
  - Runs [FastMaskRCNN] code to:
    - Generate annotations
    - Train the network

# Prerequisite
  - Install Docker

# Installation
```sh
$ cd MaskRCNN_Tensorflow_Docker
$ ./install.py

Training with CPU:
$ sudo nvidia-docker exec -it user-mrcnn_tf1.1 bash -c "cd MRCNN/FastMaskRCNN; export CUDA_VISIBLE_DEVICES= ; python train/train.py"

Training with GPU:
$ sudo nvidia-docker exec -it user-mrcnn_tf1.1 bash -c "cd MRCNN/FastMaskRCNN; python train/train.py"
```

# Notes
  - Using Tensorflow 1.1 (due to issue [#88] with TF 1.2)
  - Tested on
    - Ubuntu 	16.04.1	x86_64
    - Kernel	4.8.0-56-generic
    - Cuda 	  8.0.61
    - CuDNN	  5.1.10

# Acknowledgment
- [FastMaskRCNN]
- [Tensorflow]
- [Nvidia-docker]

[//]: #
[FastMaskRCNN]: https://github.com/CharlesShang/FastMaskRCNN
[Tensorflow]: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/docker
[Tensorflow_gpu]: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/docker
[Nvidia-docker]: https://github.com/NVIDIA/nvidia-docker
[#88]: https://github.com/CharlesShang/FastMaskRCNN/issues/88

MaskRCNN_Tensorflow_Docker

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