Hi all! I'm trying to train a model for food101 using the using the CSWin_64_12211_tiny_224 model with its pretrained values. The thing is, during execution it looks like its training from 0 rather than reusing the pretrained weights. By this I mean the initial top5 accuracy is around 5% but my initial thoughts is that it should be higher than this.
For this I loaded the pretrained model and changed it's classification layer in a separate script and saved it for use as follows
model = create_model( 'CSWin_64_12211_tiny_224', pretrained=True, num_classes=1000, drop_rate=0.0, drop_connect_rate=None, # DEPRECATED, use drop_path drop_path_rate=0.2, drop_block_rate=None, global_pool=None, bn_tf=False, bn_momentum=None, bn_eps=None, checkpoint_path='', img_size=224, use_chk=True)
chk_path = './pretrained/cswin_tiny_224.pth'
load_checkpoint(model, chk_path)
model.reset_classifier(101, 'max')
These are some of the runs I tried
`
bash finetune.sh 1 --data ../food-101 --model CSWin_64_12211_tiny_224 -b 32 --lr 5e-6 --min-lr 5e-7 --weight-decay 1e-8 --amp --img-size 224 --warmup-epochs 0 --model-ema-decay 0.9998 --epochs 20 --mixup 0.1 --cooldown-epochs 10 --drop-path 0.7 --ema-finetune --lr-scale 1 --cutmix 0.1 --use-chk --num-classes 101 --pretrained --finetune ./pretrained/CSWin_64_12211_tiny_224101.pth
bash finetune.sh 1 --data ../food-101 --model CSWin_64_12211_tiny_224 -b 32 --lr 2e-3 --weight-decay .05 --amp --img-size 224 --warmup-epochs 0 --model-ema-decay 0.9998 --epochs 20 --cooldown-epochs 10 --drop-path 0.2 --ema-finetune --cutmix 0.1 --use-chk --num-classes 101 --initial-checkpoint ./pretrained/CSWin_64_12211_tiny_224101.pth --lr-scale 1.0 --output ./full_base
`
Is there something I'm missing or a proper way I should try this?
Thanks in advance for any help! :)
Hi all! I'm trying to train a model for food101 using the using the CSWin_64_12211_tiny_224 model with its pretrained values. The thing is, during execution it looks like its training from 0 rather than reusing the pretrained weights. By this I mean the initial top5 accuracy is around 5% but my initial thoughts is that it should be higher than this.
For this I loaded the pretrained model and changed it's classification layer in a separate script and saved it for use as follows
model = create_model( 'CSWin_64_12211_tiny_224', pretrained=True, num_classes=1000, drop_rate=0.0, drop_connect_rate=None, # DEPRECATED, use drop_path drop_path_rate=0.2, drop_block_rate=None, global_pool=None, bn_tf=False, bn_momentum=None, bn_eps=None, checkpoint_path='', img_size=224, use_chk=True)chk_path = './pretrained/cswin_tiny_224.pth'load_checkpoint(model, chk_path)model.reset_classifier(101, 'max')These are some of the runs I tried
`
bash finetune.sh 1 --data ../food-101 --model CSWin_64_12211_tiny_224 -b 32 --lr 5e-6 --min-lr 5e-7 --weight-decay 1e-8 --amp --img-size 224 --warmup-epochs 0 --model-ema-decay 0.9998 --epochs 20 --mixup 0.1 --cooldown-epochs 10 --drop-path 0.7 --ema-finetune --lr-scale 1 --cutmix 0.1 --use-chk --num-classes 101 --pretrained --finetune ./pretrained/CSWin_64_12211_tiny_224101.pth
bash finetune.sh 1 --data ../food-101 --model CSWin_64_12211_tiny_224 -b 32 --lr 2e-3 --weight-decay .05 --amp --img-size 224 --warmup-epochs 0 --model-ema-decay 0.9998 --epochs 20 --cooldown-epochs 10 --drop-path 0.2 --ema-finetune --cutmix 0.1 --use-chk --num-classes 101 --initial-checkpoint ./pretrained/CSWin_64_12211_tiny_224101.pth --lr-scale 1.0 --output ./full_base
`
Is there something I'm missing or a proper way I should try this?
Thanks in advance for any help! :)