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
team | score |
---|---|
xiong (ours) | 0.80426 |
base | 0.79759 |
USTC-IAT- United | 0.79059 |
Our code are based on metaformer