Reimplementation of DeepLabV3 Semantic Segmentation
This is an (re-)implementation of DeepLabv3 -- Rethinking Atrous Convolution for Semantic Image Segmentation in TensorFlow for semantic image segmentation on the PASCAL VOC dataset. The implementation is based on DrSleep's implementation on DeepLabV2 and CharlesShang's implementation on tfrecord.
- Tensorflow support
- Multi-GPUs on single machine (synchronous update)
- Multi-GPUs on multi servers (asynchronous update)
- ImageNet pre-trained weights
- Pre-training on MS COCO
- Evaluation on VOC 2012
- Multi-scale evaluation on VOC 2012
python 3.5
tensorflow 1.4
CUDA 8.0
cuDNN 6.0
python 3.5
tensorflow 1.2
CUDA 8.0
cuDNN 5.1
The code written in Tensorflow 1.4 are compatible with Tensorflow 1.2, tested on single GPU machine.
sh setup.sh
- Configurate
config.py. - Run
python3 convert_voc12.py --split-name=SPLIT_NAME, this will generate a tfrecord file in$DATA_DIRECTORY/records. - Single GPU: Run
python3 train_voc12.py(with validation mIOU every SAVE_PRED_EVERY).
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. )
I continued training with learning rate 0.0001, there is a huge increase on validation mIOU.
There was an improvement on the implementation of Multi-grid, thanks @howard-mahe. The new validation results should be updated soon.
The new validation result was trained from scratch. I didn't implement the two stage training policy (fixing BN and stride 16 -> 8). I may try few more runs to see if there is an improvement on the performance, but I think it is a fine-tuning work.
| mIOU | Validation |
|---|---|
| paper | 77.21% |
| repo | 70.63% |
The validation mIOU for this repo is achieved without multi-scale and left-right flippling.
The improvement can be 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.
Welcome to try and report your numbers.