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DINO Training with Swin-small #334
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Hello, would u like to provide more info about your training config:
And I think you don't have to half the batch_size and learning rate, you can use gradient_checkpoint to lower the gpu memory usage and keep the batch_size the same for training. |
train.init_checkpoint = "./configs/dino-swin/swin_small_patch4_window7_224_22kto1k_finetune.pth" train.max_iter = 180000 optimizer.lr = 5e-5 dataloader.train.total_batch_size = 8 other config is the same as "dino_swin_small_224_4scale_12ep.py" |
@rentainhe excuse me,Have you encountered similar problems when loading swin-s weights |
Dear author,
Hello.
I am now training dino, the swin-s chosen by the backbone. My configuration is the same as yours, 4gpus, but my batch_size is halved to 8, so the initial learning rate is halved, but the training results are all 0.
"d2.checkpoint.c2_model_loading WARNING: Shape of norm.weight in checkpoint is torch.Size([768]), while shape of necks.norm.weight in model is torch.Size([256]) "
"d2.checkpoint.c2_model_loading WARNING: Shape of norm.weight in checkpoint is torch.Size([768]), while shape of transformer.decoder.norm.weight in model is torch.Size([256])"I downloaded from techches website weight directly, is this why?
Please don't hesitate to enlighten me!
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