This is an on-going PyTorch re-implementation of the Axial-DeepLab paper. The re-implementation is mainly done by an amazing senior student, Huaijin Pi.

@inproceedings{wang2020axial,
  title={Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation},
  author={Wang, Huiyu and Zhu, Yukun and Green, Bradley and Adam, Hartwig and Yuille, Alan and Chen, Liang-Chieh},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2020}
}

Currently, only ImageNet classification with the "Conv-Stem + Axial-Attention" backbone is supported. If you are interested in contributing to this repo, please open an issue and we can further discuss.

pip install tensorboardX
mkdir data
cd data
ln -s path/to/dataset imagenet

• Non-distributed training

python train.py --model axial50s --gpu_id 0,1,2,3 --batch_size 128 --val_batch_size 128 --name axial50s --lr 0.05 --nesterov

• Distributed training

CUDA_VISIBLE_DEVICES=0,1,2,3 python dist_train.py --model axial50s --batch_size 128 --val_batch_size 128 --name axial50s --lr 0.05 --nesterov --dist-url 'tcp://127.0.0.1:4128' --dist-backend 'nccl' --multiprocessing-distributed --world-size 1 --rank 0

You can change the model name to train different models.

python train.py --model axial50s --gpu_id 0,1,2,3 --batch_size 128 --val_batch_size 128 --name axial50s --lr 0.05 --nesterov --test

You can test with distributed settings in the same way.

• ImageNet training script is modified from https://github.com/mit-han-lab/proxylessnas

• ImageNet distributed training script is modified from https://github.com/pytorch/examples/tree/master/imagenet

• ResNet is modified from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py