Comments (1)
You can refer to https://github.com/keyu-tian/SparK/tree/main/pretrain#debug-on-1-gpu-without-distributeddataparallel.
But if pretrained from scratch, it may be difficult to achieve similar performance to our published results using only 1 GPU (those were pretrained for at least 1000 GPU hours)
So it is recommended to load our pretrained model weights and then pretrain them on your dataset for some more time, or just finetune them. You can see https://github.com/keyu-tian/SparK/tree/main/pretrain#tutorial-for-pretraining-your-own-dataset or #20 for how to pretrain on your dataset.
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Related Issues (20)
- There is no activation after the 2nd Conv in each decoder block HOT 1
- Target dataset and augmentation HOT 2
- 对比convnextv2 HOT 1
- reducing pre-training to 200 epochs HOT 9
- Tutorial for finetune on my own dataset HOT 1
- Are there any plans to make a port to tensorflow and Keras? HOT 1
- ImageNet finetuning exploding HOT 9
- there is no requirements.txt file. HOT 1
- SparK for semantic segmentation HOT 3
- Resuming ImageNet fine-tuning HOT 2
- About sparse convolution HOT 4
- How to transfer this method to 3D situation. HOT 1
- ConvNext B for reconstruct images HOT 3
- recommend a great library designed for sparse tensors HOT 1
- Can SparK be used for few-shot learning? HOT 2
- SparseBatchNorm2d can not mask correctly ? HOT 3
- A Code Issue About “pretrain/main.py” HOT 2
- SparK ResNet and global feature interaction HOT 8
- ConvNext implementation performance HOT 4
- Increasing batch size HOT 1
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