Comments (4)
pengzhiliang
commented on June 26, 2024
Yes, maybe the visualization is a good start.
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thebestYezhang
commented on June 26, 2024
不好意思，我没懂您的意思。是这样的，我想加载你们的MAE训练好的模型，也就是pretrain_mae_vit_base_mask_0.75_400e.pth这个权重。然后取出encoder部分进行finetune，然后去做我的分类任务，这样子应该怎么操作呢？指导一下，感激不尽！！！
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pengzhiliang
commented on June 26, 2024
Ok. You can fine tune it on your dataset.
Please reference: run_class_finetuning.py
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zsddd
commented on June 26, 2024
不好意思，我没懂您的意思。是这样的，我想加载你们的MAE训练好的模型，也就是pretrain_mae_vit_base_mask_0.75_400e.pth这个权重。然后取出encoder部分进行finetune，然后去做我的分类任务，这样子应该怎么操作呢？指导一下，感激不尽!!!
你好，请问你这个分类任务成功的吗？可否提供下代码，感谢
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Related Issues (20)
- pil_loader slowly
- typo error local-rank HOT 1
- TypeError: __init__() got an unexpected keyword argument 'pretrained_cfg'
- Do I need to specify the value of mask_ratio before finetune?
- training with 400 epoch has IndexError when training at the last iteration
- Which dataset is used for the released pretrained model?
- A warning when pretraining HOT 2
- Pretrained weight of vit-S
- Patch size for pretraining
- learning rate curve
- Visualize Problems HOT 2
- How to resume from the checkpoint?
- SimMIM test
- I wonder if you plan to release the mask prediction visualization code?
- RuntimeError: Given normalized_shape=[768], expected input with shape [*, 768], but got input of size[12]
- Visual loading model error HOT 6
- How to implement Layer-wise learning rate decay on ResNet?
- Error reported in code finetune, AttributeError: 'VisionTransformer' object has no attribute 'get_num_layers'.
- The import accimage cannot be parsed
- Is Mixup necessary for MAE fine-tuning?
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