xalelax / AutoML

AutoDeeplab / auto-deeplab / Hierarchical Neural Architecture Search implemented in Pytorch

AutoML for Image Segmentation and Detection

This is an open-source project of AutoML for object detection & segmentation as well as semantic segmentation.

Currently this repo contains a pytorch implementation for Auto-Deeplab.

Following the popular trend of modern CNN architectures having a two level hierarchy. Auto-Deeplab forms a dual level search space, searching for optimal network and cell architecture.

Auto-Deeplab acheives a better performance while minimizing the size of the final model.

Requirements

• Pytorch version 1.1

• Python 3

• tensorboardX

• torchvision

• pycocotools

• tqdm

• numpy

• pandas

Training

Start training

CUDA_VISIBLE_DEVICES=0 python train_autodeeplab.py --dataset cityscapes

Resume training

CUDA_VISIBLE_DEVICES=0 python train_autodeeplab.py --dataset cityscapes --resume /AutoDeeplabpath/checkpoint.pth.tar

Multi-GPU training

CUDA_VISIBLE_DEVICES=0,1 python train_autodeeplab.py --dataset cityscapes --batch_size 2

References

[1] : Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation

[2] : pytorch-deeplab-xception

[3] : Some code for the project was taken from here