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cd_v2.sh
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cd_v2.sh
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# !/usr/bin/env bash
# general settings
for fold in 0; do
train_root="datasets/data/train_${fold}" # 这里改成存放训练集和测试集的文件目录
val_root="datasets/data/val_${fold}" # 这里改成存放训练集和测试集的文件目录
batch_size=4
val_batch_size=4
epochs=50
lr=0.0001
weight_decay=0.0001
optimizer="adamw"
# pretrain_from="weights/snunet-40.pt"
# train1-ChangeModel+hrnet_w30
exp_name="fold${fold}"
model="FCEF"
backbone="FCEF"
python train_v2_1.py \
--exp_name $exp_name \
--train_root $train_root \
--val_root $val_root \
--batch_size $batch_size \
--val_batch_size $val_batch_size \
--epochs $epochs \
--lr $lr \
--weight_decay $weight_decay \
--backbone $backbone \
--model $model \
--optimizer $optimizer
done
# # train2-ChangeModel+resnext50_32x4d
# exp_name="change_v2"
# model="ChangeModel"
# backbone="resnext50_32x4d"
# python train_v1.py \
# --exp_name $exp_name \
# --data_root $data_root \
# --batch_size $batch_size \
# --val_batch_size $val_batch_size \
# --epochs $epochs \
# --lr $lr \
# --weight_decay $weight_decay \
# --backbone $backbone \
# --model $model \
# --optimizer $optimizer
# # --pretrain_from $pretrain_from
# # train3-Siamese+hrnet_w30
# exp_name="change_v3"
# model="Siamese"
# backbone="hrnet_w30"
# python train_v2.py \
# --exp_name $exp_name \
# --data_root $data_root \
# --batch_size $batch_size \
# --val_batch_size $val_batch_size \
# --epochs $epochs \
# --lr $lr \
# --weight_decay $weight_decay \
# --backbone $backbone \
# --model $model \
# --optimizer $optimizer
# # train4-Siamese+resnext50_32x4d
# exp_name="change_v4"
# model="Siamese"
# backbone="resnext50_32x4d"
# python train_v2.py \
# --exp_name $exp_name \
# --data_root $data_root \
# --batch_size $batch_size \
# --val_batch_size $val_batch_size \
# --epochs $epochs \
# --lr $lr \
# --weight_decay $weight_decay \
# --backbone $backbone \
# --model $model \
# --optimizer $optimizer
# # test
# # python test.py $data_root