InaR-design
commented
3 months ago Hello, I trained cifar10-lt and cifar100-lt with the default parameters in the code, but the results I obtained are quite different from those in the paper. Could you please tell me how can I achieve the results in the paper? Could you provide your commands, or are there any other parameters or settings that need to be noted? This is what I used for training：
- train：
python train.py --gpu 0 --ds cifar100 --Lambda1 0.05 --Lambda2 0.05 --Lambda3 0.1 --drp ../long-tailed-ood-detection/SCOOD_dataset/data/images --srp ./checkpoints- test：'python test.py --gpu 0 --ds cifar100 --dout shvn --drp ../long-tailed-ood-detection/SCOOD_dataset/data/images --ckpt_path ./checkpoints/cifar100-0.01-OOD300000/ResNet18/e100-b128-256-adam-lr0.001-wd0.0005_Lambda10.05-Lambda20.05-Lambda30.1/replay3' and this is the result i got：
Hello, you should set -OLC when test, but the results of OOD detection still differ from those reported in the paper :/
Hello, I trained cifar10-lt and cifar100-lt with the default parameters in the code, but the results I obtained are quite different from those in the paper. Could you please tell me how can I achieve the results in the paper? Could you provide your commands, or are there any other parameters or settings that need to be noted? This is what I used for training：
python train.py --gpu 0 --ds cifar100 --Lambda1 0.05 --Lambda2 0.05 --Lambda3 0.1 --drp ../long-tailed-ood-detection/SCOOD_dataset/data/images --srp ./checkpoints