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fgvc9_fungiclef

steps

train

python3 -m torch.distributed.launch --nproc_per_node 4 --master_port 12345  main.py --cfg ./configs/MetaFG_meta_0_384.yaml --batch-size 64 --tag ${EXP_TAG} --lr 5e-5 --min-lr 5e-7 --warmup-lr 5e-8 --epochs 64 --warmup-epochs 1 --dataset fungi --pretrain ./pretrained_model/metafg_0_inat21_384.pth --accumulation-steps 4 --num-workers 16 --opts DATA.IMG_SIZE 384

test

python3 -m torch.distributed.launch --nproc_per_node 4 --master_port 12344  main.py --eval --cfg ./configs/MetaFG_meta_0_384.yaml --dataset fungi_test --resume output/MetaFG_meta_0/${EXP_TAG}/latest.pth --batch-size 64 --tag ${EXP_TAG}_test --opts DATA.IMG_SIZE 384

ensamble and post process

After runing test, we will get result{0-rank}.pkl which indicate the output of a single model, we can average ensamble the model outputs and do post process by runing python post_avg.py

results

team score
xiong (ours) 0.80426
base 0.79759
USTC-IAT- United 0.79059

Our code are based on metaformer

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