Apache License 2.0

项目介绍

CharlesShang/FastMaskRCNN 是一个托管在 GitHub 上的开源项目,主要使用 Python 编写,采用 Apache License 2.0 许可证,目前获得 3.1k 个 Star、1.1k 次 Fork。

仓库全名
CharlesShang/FastMaskRCNN
开源许可证
Apache License 2.0
Star 数
3.1k
Fork 数
1.1k
未关闭 Issue
142

README 摘要

Mask RCNN Mask RCNN in TensorFlow This repo attempts to reproduce this amazing work by Kaiming He et al. : Mask R-CNN Requirements Tensorflow (>= 1.0.0) Numpy COCO dataset Resnet50 How-to 1. Go to ./libs/datasets/pycocotools and run make 2. Download COCO dataset, place it into ./data, then run python downloadandconvertdata.py to build tf-records. It takes a while. 3. Download pretrained resnet50 model, wget…

完整内容见 GitHub 原始 README 或本页 。

常见问题

FastMaskRCNN 用什么语言开发?

主要使用 Python 编写,采用 Apache License 2.0 许可证,目前有 3.1k 个 Star。

FastMaskRCNN 可以免费商用吗?

仓库采用 Apache License 2.0 许可证,可免费使用与修改,商业使用请遵守许可证条款(例如保留版权声明与许可声明)。

如何获取 FastMaskRCNN 的源码?

可以直接克隆仓库:git clone https://github.com/CharlesShang/FastMaskRCNN.git,默认分支为 master;安装与运行步骤见仓库 README。

FastMaskRCNN 最近还在维护吗?

仓库最近一次更新为 2021年1月5日,当前有 142 个未关闭 Issue;Star 数为 3.1k,可作为社区活跃度的参考。

简要分析

热度分
5452
价值分
2186
活跃状态
活跃

语言占比

C
C++
Cuda
Makefile
Python
Shell

README

Mask RCNN

Mask RCNN in TensorFlow

This repo attempts to reproduce this amazing work by Kaiming He et al. : Mask R-CNN

Requirements

How-to

  1. Go to ./libs/datasets/pycocotools and run make
  2. Download COCO dataset, place it into ./data, then run python download_and_convert_data.py to build tf-records. It takes a while.
  3. Download pretrained resnet50 model, wget http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz, unzip it, place it into ./data/pretrained_models/
  4. Go to ./libs and run make
  5. run python train/train.py for training
  6. There are certainly some bugs, please report them back, and let’s solve them together.

TODO:

  • ROIAlign
  • COCO Data Provider
  • Resnet50
  • Feature Pyramid Network
  • Anchor and ROI layer
  • Mask layer
  • Speedup anchor layer with cython
  • Combining all modules together.
  • Testing and debugging (in progress)
  • Training / evaluation on COCO
  • Add image summary to show some results
  • Converting ResneXt
  • Training >2 images

Call for contributions

  • Anything helps this repo, including discussion, testing, promotion and of course your awesome code.

Acknowledgment

This repo borrows tons of code from

License

See LICENSE for details.

评论

暂无评论