I attempted to continue training on the resnet34.a1_in1k weights using the following parameters: --data /dataset/imagenet --model resnet34rer -b 128 --lr 1e-6 --weight-decay 0.01 --aa rand-m7-mstd0.5-inc1 --cutmix 0.2 --color-jitter 0. --amp --warmup-lr 1e-6 --experiment resnet34rer --opt lamb --initial-checkpoint a1weight/resnet34_a1_0-46f8f793.pth --bce-loss --grad-accum-steps 2
However, it was found that the learning is not continuous, and the accuracy has a 0.5% decrease.
Could you please provide the relevant scripts for this training?
I attempted to continue training on the resnet34.a1_in1k weights using the following parameters: --data /dataset/imagenet --model resnet34rer -b 128 --lr 1e-6 --weight-decay 0.01 --aa rand-m7-mstd0.5-inc1 --cutmix 0.2 --color-jitter 0. --amp --warmup-lr 1e-6 --experiment resnet34rer --opt lamb --initial-checkpoint a1weight/resnet34_a1_0-46f8f793.pth --bce-loss --grad-accum-steps 2
However, it was found that the learning is not continuous, and the accuracy has a 0.5% decrease.
Could you please provide the relevant scripts for this training?