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cannot reproduce the reported results #3
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Hi, thanks for your reply, resetting the lr of finetuning works, the best mean Dice of 5 folds of mine is 0.8421(0.025) , I think it's a normal bias, thanks again for your wonderful work. |
But, with only labeled dataset(100 patients), the best mean Dice(sample_k=6) is 0.7858, which has no improvement against the baseline(0.7883). |
@CaiziLee For your questions:
|
a very large gap between the reported results and mines, did I miss any important tricks?
The details of my experiments are presented as follows,
1, fix two bugs:
2, prepare acdc dataset via generate_acdc.py
3, prepare running scripts:
(1) from scratch:
python train_supervised.py --device cuda:0 --batch_size 10 --epochs 200 --data_dir ./dir_for_labeled_data --lr 5e-4 --min_lr 5e-6 --dataset acdc --patch_size 352 352 --experiment_name supervised_acdc_random_sample_6_ --initial_filter_size 48 --classes 4 --enable_few_data --sampling_k 6;
(2) contrastive learning:
python train_contrast.py --device cuda:0 --batch_size 32 --epochs 300 --data_dir ./dir_for_unlabeled_data --lr 0.01 --do_contrast --dataset acdc --patch_size 352 352 --experiment_name contrast_acdc_pcl_temp01_thresh035_ --slice_threshold 0.35 --temp 0.1 --initial_filter_size 48 --classes 512 --contrastive_method pcl;
python train_contrast.py --device cuda:0 --batch_size 32 --epochs 300 --data_dir ./dir_for_unlabeled_data --lr 0.01 --do_contrast --dataset acdc --patch_size 352 352 --experiment_name contrast_acdc_gcl_temp01_thresh035_ --slice_threshold 0.35 --temp 0.1 --initial_filter_size 48 --classes 512 --contrastive_method gcl;
python train_contrast.py --device cuda:0 --batch_size 32 --epochs 300 --data_dir ./dir_for_unlabeled_data --lr 0.01 --do_contrast --dataset acdc --patch_size 352 352 --experiment_name contrast_acdc_simclr_temp01_thresh035_ --slice_threshold 0.35 --temp 0.1 --initial_filter_size 48 --classes 512 --contrastive_method simclr;
(3) finetuning:
python train_supervised.py --device cuda:0 --batch_size 10 --epochs 100 --data_dir ./dir_for_labeled_data --lr 5e-5 --min_lr 5e-6 --dataset acdc --patch_size 352 352 --experiment_name supervised_acdc_simclr_sample_6_ --initial_filter_size 48 --classes 4 --enable_few_data --sampling_k 6 --restart --pretrained_model_path ./results/contrast_acdc_simclr_temp01_thresh035_2021-12-05_09-43-38/model/latest.pth;
python train_supervised.py --device cuda:1 --batch_size 10 --epochs 100 --data_dir ./dir_for_labeled_data --lr 5e-5 --min_lr 5e-6 --dataset acdc --patch_size 352 352 --experiment_name supervised_acdc_gcl_sample_6_ --initial_filter_size 48 --classes 4 --enable_few_data --sampling_k 6 --restart --pretrained_model_path ./results/contrast_acdc_gcl_temp01_thresh035_2021-12-04_03-46-35/model/latest.pth;
python train_supervised.py --device cuda:1 --batch_size 10 --epochs 100 --data_dir ./dir_for_labeled_data --lr 5e-5 --min_lr 5e-6 --dataset acdc --patch_size 352 352 --experiment_name supervised_acdc_pcl_sample_6_ --initial_filter_size 48 --classes 4 --enable_few_data --sampling_k 6 --restart --pretrained_model_path ./results/contrast_acdc_pcl_temp01_thresh035_2021-12-02_21-48-13/model/latest.pth;
4, experimental results(ubuntu16.04, pytorch1.9, NVIDIA 2080Ti * 2, Dice metric, take sample_k=6 as an example):
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