DeepLab v3+ in MXNet Gluon

• train_multi_gpu.py: multi-gpu training on Pascal VOC dataset, with validation.
• train.py: single-gpu training on Pascal VOC dataset, with validation.
• evaluate.py: single-gpu evaluation on Pascal VOC validation.
• extract_weights.py: convert the weights from official model release.
• mylib: lib-style clean code.
• workspace: the notebooks where I did experiments, with messy staffs (ignore them).
• GPU version only, but it should be modified easily into a CPU version.
• My running environments, not tested with other environments:
  • Python==3.6
  • MXNet>=1.2.0 (MXNet==1.3.0 for multi-gpu SyncBatchNorm)
  • gluoncv==0.3.0
  • TensorFlow==1.4.0, Keras==2.1.5 (for converting the weights)
• Download the dataset

git clone https://github.com/dmlc/gluon-cv
cd gluon-cv/scripts/datasets
python pascal_voc.py

My porting on Pascal VOC validation:

Measured with fixing batch stats (use_global_stats=True), just for reference.

• transfer all the weights
• add OS=8
• test iou on PASCAL val
• add training scripts
• add multi-gpu training scripts
• train more and open source the best models
• VOCAug dataset pull request
• Model pull request
• Finish pull request to gluoncv

This repository is a part of MXNet summer code hosted by AWS, TuSimple and Jiangmen. Specifically, I would like to thank Hang Zhang (@AWS) and Hengchen Dai (@TuSimple) for kind suggestions on tuning and implementation. Plus, I would like to thank AWS for providing generous credits for tuning the computationally intensive models.