Comments (2)
Distributed learning is used for parallel training. If you don't want to train on multiple GPUs or if using nn.DataParallel is enough for your case. Then delete all the use of "dist" in the file.
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Has anyone found a solution to the problem I'm experiencing too?
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Related Issues (20)
- can not reproduce your results. trained from your released pre-trained vit-base model HOT 1
- SimMIM with Absolute Position Embedding
- when training, here happens 'Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to' HOT 1
- a bug in loading pretrain checkpoint , in utils.py line 118, you dont use checkpoint in line 111
- For cnn architecture like resnet50
- Could you please release the pre-trained R50 model?
- Why there is "no_weight_decay" function for Swin-T but not for VIT
- Train from scratch HOT 1
- Performance using the cosine distance
- Indentation bug in utils.remap_pretrained_keys_vit
- Why Swin-Large-W12 contains [36, 36] `encoder.layers.3.blocks.0.attn.relative_position_index` HOT 1
- The specific configs and code for downstream tasks, like semantic segmentation and object detection
- Any plan to support 3D version?
- Questions about AvgDist
- any plan to release the code of DDP training of swinV2-G with multi machine?
- Tt
- Setting for Linear eval
- Could you provide the pretrained ViT-Base model with patch size 16?
- 用自监督结果的预训练权重对下游任务精度影响
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