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.
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Pytorch version 1.1
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Python 3
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tensorboardX
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torchvision
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pycocotools
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tqdm
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numpy
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pandas
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
[1] : Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
[2] : pytorch-deeplab-xception