This is a PyTorch implementation of semantic segmentation models on MIT ADE20K scene parsing dataset.
Follow the link below to find the repository for our dataset and implementations on Caffe and Torch7:
https://github.com/CSAILVision/sceneparsing
- vgg16_dilated
- vgg19_dilated
- resnet34_dilated16
- resnet34_dilated8
- resnet50_dilated16
- resnet50_dilated8
- Download the ADE20K scene parsing dataset:
chmod +x download_ADE20K.sh
./download_ADE20K.sh- Train a network: (default: ResNet34-dilated8)
python train.py- Input arguments: (see full input arguments via
python train.py -h)
usage: train.py [-h] [--id ID] [--arch_encoder ARCH_ENCODER]
[--arch_decoder ARCH_DECODER]
[--weights_encoder WEIGHTS_ENCODER]
[--weights_decoder WEIGHTS_DECODER] [--fc_dim FC_DIM]
[--list_train LIST_TRAIN] [--list_val LIST_VAL]
[--root_img ROOT_IMG] [--root_seg ROOT_SEG]
[--num_gpus NUM_GPUS]
[--batch_size_per_gpu BATCH_SIZE_PER_GPU]
[--num_epoch NUM_EPOCH] [--optim OPTIM]
[--lr_encoder LR_ENCODER] [--lr_decoder LR_DECODER]
[--lr_step LR_STEP] [--beta1 BETA1]
[--weight_decay WEIGHT_DECAY] [--fix_bn FIX_BN]
[--num_val NUM_VAL] [--workers WORKERS] [--imgSize IMGSIZE]
[--segSize SEGSIZE] [--segDepth SEGDEPTH] [--flip FLIP]
[--seed SEED] [--ckpt CKPT] [--vis VIS]
[--disp_iter DISP_ITER] [--eval_epoch EVAL_EPOCH]
[--ckpt_epoch CKPT_EPOCH]- Evaluate a trained network on the validation set:
python eval.py --id MODEL_ID- Input arguments: (see full input arguments via
python eval.py -h)
usage: eval.py [-h] --id ID [--suffix SUFFIX] [--arch_encoder ARCH_ENCODER]
[--arch_decoder ARCH_DECODER] [--fc_dim FC_DIM]
[--list_val LIST_VAL] [--root_img ROOT_IMG]
[--root_seg ROOT_SEG] [--num_val NUM_VAL]
[--batch_size BATCH_SIZE] [--imgSize IMGSIZE]
[--segSize SEGSIZE] [--segDepth SEGDEPTH] [--ckpt CKPT]
[--visualize VISUALIZE] [--result RESULT]- Do inference on a single image:
python test.py --id MODEL_ID --test_img TEST_IMG- Input arguments: (see full input arguments via
python test.py -h)
usage: test.py [-h] --id ID [--suffix SUFFIX] [--arch_encoder ARCH_ENCODER]
[--arch_decoder ARCH_DECODER] [--fc_dim FC_DIM] --test_img
TEST_IMG [--num_val NUM_VAL] [--batch_size BATCH_SIZE]
[--imgSize IMGSIZE] [--segSize SEGSIZE] [--segDepth SEGDEPTH]
[--ckpt CKPT] [--visualize VISUALIZE] [--result RESULT]If you find the code or pre-trained models useful, please cite the following paper:
Scene Parsing through ADE20K Dataset. B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso and A. Torralba. Computer Vision and Pattern Recognition (CVPR), 2017. (http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf)
@inproceedings{zhou2017scene,
title={Scene Parsing through ADE20K Dataset},
author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2017}
}
Semantic Understanding of Scenes through ADE20K Dataset. B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso and A. Torralba. arXiv:1608.05442. (https://arxiv.org/pdf/1608.05442.pdf)
@article{zhou2016semantic,
title={Semantic understanding of scenes through the ade20k dataset},
author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
journal={arXiv preprint arXiv:1608.05442},
year={2016}
}