DeepLabV3 Semantic Segmentation

Reimplementation of DeepLabV3 Semantic Segmentation

Features

• Tensorflow support
• ImageNet pre-trained weights for ResNet101
• Pre-training on MS COCO
• Evaluation on VOC 2012
• Multi-scale evaluation on VOC 2012

Train

1. Configurate config.py.
2. Run python3 convert_voc12.py --split-name=SPLIT_NAME, this will generate a tfrecord file in $DATA_DIRECTORY/records.
3. Single GPU: Run python3 train_voc12.py (with validation mIOU every SAVE_PRED_EVERY).

Performance

This repository only implements MG(1, 2, 4), ASPP and Image Pooling. The training is started from scratch. (The training took me almost 2 days on a single GTX 1080 Ti. I changed the learning rate policy in the paper: instead of the 'poly' learning rate policy, I started the learning rate from 0.01, then set fixed learning rate to 0.005 and 0.001 when the seg_loss stopped to decrease, and used 0.001 for the rest of training. )

mIOU	Validation
paper	77.21%
repo	76.98%

The validation mIOU for this repo is achieved without multi-scale and left-right flippling.

The improvement can be also achieved by finetuning on hyperparameters such as learning rate, batch size, optimizer, initializer and batch normalization. I didn't spend too much time on training and the results are temporary.

Reference

• NanqingD/DeepLabV3-Tensorflow
• DeepLabv3 -- Rethinking Atrous Convolution for Semantic Image Segmentation
• PASCAL VOC dataset.
• DrSleep's implementation on DeepLabV2
• CharlesShang's implementation on tfrecord