examples/image-classification/run.sh

# ✅ --lr 0.00002 Acc@1 71.878 Acc@5 90.286 ->  Acc@1 72.104 Acc@5 90.316  (with normalization)
torchrun --nproc_per_node=4 train.py\
    --data-path /home/cs/Documents/datasets/imagenet\
    --model mobilenet_v2  --output-dir mobilenet_v2 --weights MobileNet_V2_Weights.IMAGENET1K_V1\
    --batch-size 192 --epochs 40 --lr 0.0004 --lr-step-size 10 --lr-gamma 0.5 --wd 0.00004 --apply-trp --trp-depths 1 --trp-p 0.15 --trp-lambdas 0.4 0.2 0.1
# torchrun --nproc_per_node=4 train.py\
#     --data-path /home/cs/Documents/datasets/imagenet\
#     --model mobilenet_v2  --resume mobilenet_v2/model_32.pth --test-only


# ✅ --lr 0.0002 Acc@1 76.130 Acc@5 92.862 -> Acc@1 77.234 Acc@5 93.322  (with normalization)
torchrun --nproc_per_node=4 train.py\
    --data-path /home/cs/Documents/datasets/imagenet\
    --model resnet50 --output-dir resnet50 --weights ResNet50_Weights.IMAGENET1K_V1\
    --batch-size 64 --epochs 40 --lr 0.0004 --lr-step-size 10 --lr-gamma 0.5 --print-freq 100\
    --apply-trp --trp-depths 1 --trp-p 0.2 --trp-lambdas 0.4 0.2 0.1
# torchrun --nproc_per_node=4 train.py\
#     --data-path /home/cs/Documents/datasets/imagenet\
#     --model resnet50  --resume resnet50/model_35.pth --test-only


# ✅ Test:  Acc@1 85.218 Acc@5 97.208
torchrun --nproc_per_node=4 train.py \
    --data-path /home/cs/Documents/datasets/imagenet\
    --model efficientnet_v2_m --output-dir efficientnet_v2_m --weights EfficientNet_V2_M_Weights.IMAGENET1K_V1\
    --epochs 10 --batch-size 64 --lr 5e-9 --lr-scheduler cosineannealinglr --weight-decay 0.00002 \
    --lr-warmup-method constant --lr-warmup-epochs 8 --lr-warmup-decay 0. \
    --auto-augment ta_wide --random-erase 0.1 --label-smoothing 0.1 --mixup-alpha 0.2 --cutmix-alpha 1.0 --norm-weight-decay 0.0 \
    --train-crop-size 384 --val-crop-size 480 --val-resize-size 480 --ra-sampler --ra-reps 4 --print-freq 100\
    --apply-trp --trp-depths 1 --trp-p 0.2 --trp-lambdas 0.4 0.2 0.1
# torchrun --nproc_per_node=4 train.py\
#     --data-path /home/cs/Documents/datasets/imagenet\
#     --model efficientnet_v2_m  --resume efficientnet_v2_m/model_7.pth --test-only\
#      --val-crop-size 480 --val-resize-size 480


# ✅ Test:  Acc@1 81.092 Acc@5 95.304
torchrun --nproc_per_node=4 train.py\
    --data-path /home/cs/Documents/datasets/imagenet\
    --model vit_b_16 --output-dir vit_b_16 --weights ViT_B_16_Weights.IMAGENET1K_V1\
    --epochs 5 --batch-size 196 --opt adamw --lr 5e-9 --lr-scheduler cosineannealinglr --wd 0.3\
    --lr-warmup-method constant --lr-warmup-epochs 3 --lr-warmup-decay 0. \
    --amp --label-smoothing 0.11 --mixup-alpha 0.2 --auto-augment ra --clip-grad-norm 1 --cutmix-alpha 1.0\
    --apply-trp --trp-depths 1 --trp-p 0.1 --trp-lambdas 0.4 0.2 0.1 --print-freq 100
# torchrun --nproc_per_node=4 train.py\
#     --data-path /home/cs/Documents/datasets/imagenet\
#     --model vit_b_16  --resume vit_b_16/model_4.pth --test-only