This repo is aimed to simplify training, evaluation and prediction in Pytorch.
- Focus on your research rather than training template codes
- Dynamic module registration mechanism makes you customize components on the fly
- Flexible plugin mechanism for a hackable trainer without any coupling!
- High performance parallel training using Pytorch
- Support mixed precision training, significantly reducing GPU memory usage with similar performance
- Support stable distribute training and Sync BN by offical repo and NVIDIA/apex
pip install --upgrade git+https://github.com/Z-Zheng/SimpleCV.git
- pytorch >= 1.1.0
- tensorboardX
- opencv, skimage, sklearn, pillow
If you use SimpleCV in your research, please use the following BibTeX entry.
@misc{simplecv2018,
author = {Zhuo Zheng},
title = {SimpleCV},
howpublished = {\url{https://github.com/Z-Zheng/SimpleCV}},
year = {2018}
}
Please refer to USAGE.md for the basic usage of SimpleCV.
- 2019 IEEE GRSS Data Fusion Contest, Track1: Single-view semantic 3D challenge, 2nd solution code (Pop-Net)
- 2020 xView2 Assess Building Damage Challenge, 4th solution code
- Official implementation of our work: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery, CVPR 2020
- Official implementation of our work: FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification, TGRS 2020
- 2019/10/29 v0.3.4 released! More preset models have been added.
- 2019/06/25 v0.3.1 released! More features have been added.
- 2019/05/24 v0.3.0 released!
- 2019/05/05 compatible with pytorch==1.1.0 (naive sync bn in ddp train)
- 2019/04/08 v0.2.0 released! Support apex!
- 2019/01/25 summary grads and weights
- 2018/12/20 support SE Block