64 lines
1.4 KiB
Markdown
64 lines
1.4 KiB
Markdown
<!-- # Slide-SAM -->
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# Slide-SAM: Medical SAM meets sliding window
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We upload the checkpoint recently!
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Please download by
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https://pan.baidu.com/s/1jvJ2W4MK24JdpZLwPqMIfA
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code：7be9
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## Before Training
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### install tutils
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```
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pip install trans-utils
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```
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### prepare datasets
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We recommend you to convert the dataset into the nnUNet format.
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```
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00_custom_dataset
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imagesTr
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xxx_0000.nii.gz
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...
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labelsTr
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xxx.nii.gz
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...
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```
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try to use the function ```organize_in_nnunet_style``` or ```organize_by_names``` to prepare your custom datasets.
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Then run
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```
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python -m datasets.generate_txt
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```
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A ```[example]_train.txt``` will be generated in ```./datasets/dataset_list/```
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The content should be like below
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```
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01_BCV-Abdomen/Training/img/img0001.nii.gz 01_BCV-Abdomen/Training/label/label0001.nii.gz
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01_BCV-Abdomen/Training/img/img0002.nii.gz 01_BCV-Abdomen/Training/label/label0002.nii.gz
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01_BCV-Abdomen/Training/img/img0003.nii.gz 01_BCV-Abdomen/Training/label/label0003.nii.gz
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```
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### cache 3d data into slices
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After generating the ```[example]_train.txt``` file, check the config file ```configs/vit_b.yaml```.
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Update the params in ```dataset``` by yours. And the ```dataset_list``` should be the name of the generated txt file ```[example]```.
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Then run
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```
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python -m datasets.cache_dataset3d
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```
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## Training
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run training
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```
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CUDA_VISIBLE_DEVICES=0,1,2,3 python -m core.ddp --tag debug
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```
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## Testing
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```
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python -m core.volume_predictor
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```
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