A higher result than paper, but i don't know why. #4
Comments
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Hi, thanks for sharing your log with us. Did you train any other models with your dataset and are they normal? |
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due to limited gpu, I just try the vip_s7, until to now, the accuracy of vip_s7 is 84.68%。
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Have you ckeched that there is no problem with your val set? |
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Yes, I had validated the two model(one is your pretrained, the other is my retrained), the accuracy of the pretrained is 81.472（same as the paper） , while my retrained is 83.922（This is already better than vip_l7）。 CUDA_VISIBLE_DEVICES=3 python3 validate.py --model vip_s7 --checkpoint weights/vip_s7.pth --no-test-pool --amp --img-size 224 -b 64 CUDA_VISIBLE_DEVICES=5 python3 validate.py --model vip_s7 --checkpoint output/train/20210712-083848-vip_s7-224/model_best.pth.tar --no-test-pool --amp --img-size 224 -b 64 |
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We are trying to reproduce the issue. The main difference is that you are using a very small batch size (384) while we are using a large batch size (2048). python3 validate.py /path/to/imagenetv2 --model vip_s7 --checkpoint /path/to/checkpoint --no-test-pool --amp --img-size 224 -b 64to see if the Top-1 acc is around 74% (70.9% for our pretrained vip_s7). If so, this is likely to be a much better training receipt for ViP. |
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up to now, i have no imagenetv2 datasets. If possible, you can add my Wechat: AIWalker-Happy |
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Hi, Is there any new progress on this result? The ViP_s7 got the 85.16 ImageNet top-1 or Real top-1? |
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Any news? Big news, or big bug? |
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Seems that there are some problems with the training data |
I don't know why,but I got a 84.42%@vip_s7，the following is the cmd and log.
Note: I train the vip with 6 Ti1080, batch=64.
CUDA_VISIBLE_DEVICES=0,1,2,4,6,7 ./distributed_train.sh 6 --data /data/path/to/imagenet --model vip_s7 -b 64 -j 6 --opt adamw --epochs 300 --sched cosine --apex-amp --img-size 224 --drop-path 0.1 --lr 2e-3 --weight-decay 0.05 --remode pixel --reprob 0.25 --aa rand-m9-mstd0.5-inc1 --smoothing 0.1 --mixup 0.8 --cutmix 1.0 --warmup-lr 1e-6 --warmup-epochs 20
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