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ShikunLi / Sel-CL

CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels
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about top1 Accuracy on dataset 'CIFAR-10' #4

Open Hlic818 opened 2 years ago

Hlic818 commented 2 years ago

I just got 91.31 % top1 Accuracy on dataset 'CIFAR-10' which was 95.5% in your paper. what's the difference of experimental parameters between the original paper and the following two steps？ (1) python train_Sel-CL.py --epoch 250 --num_classes 10 --batch_size 128 --low_dim 128 --lr-scheduler "step" --noise_ratio 0.2 \ --network "PR18" --lr 0.1 --wd 1e-4 --dataset "CIFAR-10" --download False --noise_type "symmetric" \ --sup_t 0.1 --headType "Linear" --sup_queue_use 1 --sup_queue_begin 3 --queue_per_class 1000 \ --alpha 0.5 --beta 0.25 --k_val 250 --experiment_name CIFAR10 --cuda_dev 0 --alpha_m 1.0 --seed_initialization 1 --seed_dataset 42 \ --uns_t 0.1 --uns_queue_k 10000 --lr-warmup-epoch 5 --warmup-epoch 1 --lambda_s 0.01 --lambda_c 1 --warmup_way 'uns' (2) python train_Sel-CL_fine-tuning.py --epoch 70 --num_classes 10 --batch_size 128 --noise_ratio 0.2 \ --network "PR18" --lr 0.001 --wd 1e-4 --dataset "CIFAR-10" --cuda_dev 0 \ --headType "Linear" --noise_type "symmetric" --DA "Simple" --ReInitializeClassif 1 \ --startLabelCorrection 30 --alpha_m 1.0 --seed_initialization 1 --seed_dataset 42 \ --experiment_name CIFAR10 --train_root ./dataset --out ./out

Dokumushikun commented 1 year ago

I got 88.55 % on CIFAR-10 dataset with noise_ratio = 0.4.

miladdehghani1 commented 1 year ago

I got 88.55 % on CIFAR-10 dataset with noise_ratio = 0.4. How much RAM is needed to run the codes of this article? (I use COLAB to run this code, but RAM crashes)