Pascal VOC 2012

CUDA_VISIBLE_DEVICES=0 python main1.py --batch-size 64 --epochs 60 --reduction 8 --pool-type max --lr 0.000512 --data-set VOC12 --output_dir semples1/voc --resume checkpoint_best_mIoU.pth --num_workers 8 --img-ms-list voc12/train_id.txt --gt-dir SegmentationClass --cam-npy-dir semples1/voc/attn-patchrefine-npy --scales 1.0 --hypers 2.4 &

CUDA_VISIBLE_DEVICES=0 python main2.py --batch-size 128 --epochs 60 --reduction 8 --pool-type max --lr 0.1 --data-set VOC12 --output_dir semples2/voc --resume semples1/voc/checkpoint_best_mIoU.pth --num_workers 8 --img-ms-list voc12/train_id.txt --gt-dir SegmentationClass --cam-npy-dir semples2/voc/attn-patchrefine-npy --scales 1.0 --hypers 0.02 --btp_length 30 &

CUDA_VISIBLE_DEVICES=0 python main3.py --batch-size 64 --epochs 60 --reduction 8 --pool-type max --lr 0.000512 --data-set VOC12 --output_dir semples3/voc --resume checkpoint_best_mIoU.pth --num_workers 8 --img-ms-list voc12/train_id.txt --gt-dir SegmentationClass --cam-npy-dir semples3/voc/attn-patchrefine-npy --scales 1.0 --hypers 2.4 0.05 --btp_length 30 --btp_path semples2_0.02/voc/btp.pth &

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 --master_port=14898 main1.py --epochs 60 --reduction 8 --pool-type max --lr 0.000512 --data-set VOC12MS --output_dir semples3/voc --resume semples3/voc/checkpoint_best_mIoU.pth --num_workers 8 --gen_attention_maps --attention-dir semples3/voc/attention_maps --img-ms-list voc12/train_aug_id.txt --gt-dir SegmentationClass --cam-npy-dir semples3/voc/attn-patchrefine-npy-ms --scales 1.0 1.2 --hypers 2.4 &

python evaluation.py --list voc12/train_id.txt --data-path data --type npy --predict_dir semples3/voc/attn-patchrefine-npy-ms --out-dir semples3/voc/pseudo-mask-ms-crf --curve True --start 40 --t 43 --out-crf &




COCO 2014

CUDA_VISIBLE_DEVICES=0 python main1.py --batch-size 32 --epochs 60 --reduction 4 --pool-type avg --lr 0.00000512 --data-set COCO --output_dir semples1/coco --resume checkpoint_best_mIoU.pth --num_workers 8 --img-ms-list coco/train_1250_id.txt --gt-dir voc_format/class_labels --cam-npy-dir semples1/coco/attn-patchrefine-npy --scales 1.0 --hypers 0.75 &

CUDA_VISIBLE_DEVICES=0 python main2.py --batch-size 128 --epochs 60 --reduction 4 --pool-type avg --lr 0.01 --data-set COCO --output_dir semples2/coco --resume semples1/coco/checkpoint_best_mIoU.pth --num_workers 8 --img-ms-list coco/train_1250_id.txt --gt-dir voc_format/class_labels --cam-npy-dir semples2/voc/attn-patchrefine-npy --scales 1.0 --hypers 0.0 --btp_length 30 &

CUDA_VISIBLE_DEVICES=0 python main3.py --batch-size 24 --epochs 60 --reduction 4 --pool-type avg --lr 0.00000512 --data-set COCO --output_dir semples3/coco --resume checkpoint_best_mIoU.pth --num_workers 8 --img-ms-list coco/train_1250_id.txt --gt-dir voc_format/class_labels --cam-npy-dir semples3/coco/attn-patchrefine-npy --scales 1.0 --hypers 0.75 0.2 --btp_length 30 --btp_path semples2_0.01/coco/btp.pth &

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --master_port=14898 main1.py --epochs 60 --reduction 4 --pool-type avg --lr 0.00000512 --data-set COCOMS --output_dir semples3/coco --resume semples3/coco/checkpoint_best_mIoU.pth --num_workers 64 --gen_attention_maps --attention-dir semples3/coco/attention_maps --img-ms-list coco/train_id.txt --gt-dir voc_format/class_labels --cam-npy-dir semples3/coco/attn-patchrefine-npy-ms --scales 1.0 0.8 1.2 --hypers 0.75 &

python evaluation.py --list coco/train_id.txt --data-path data --type npy --predict_dir semples3/coco/attn-patchrefine-npy-ms --out-dir semples3/coco/pseudo-mask-ms-crf --num_classes 91 --curve True --start 40 --t 42 --out-crf &


One may refer to the code of WeakTr for training the final segmentation model and more details