# Slide-SAM: Medical SAM meets sliding window We upload the SlideSAM-H checkpoint recently! Please download by Slide-SAM-B: https://pan.baidu.com/s/1jvJ2W4MK24JdpZLwPqMIfA [code：7be9] SlideSAM-H: https://pan.baidu.com/s/1jnOwyWd-M1fBIauNi3IA4w [code: 05dy] ## Before Training ### install tutils ``` pip install trans-utils ``` ### prepare datasets We recommend you to convert the dataset into the nnUNet format. ``` 00_custom_dataset imagesTr xxx_0000.nii.gz ... labelsTr xxx.nii.gz ... ``` try to use the function ```organize_in_nnunet_style``` or ```organize_by_names``` to prepare your custom datasets. Then run ``` python -m datasets.generate_txt ``` A ```[example]_train.txt``` will be generated in ```./datasets/dataset_list/``` The content should be like below ``` 01_BCV-Abdomen/Training/img/img0001.nii.gz 01_BCV-Abdomen/Training/label/label0001.nii.gz 01_BCV-Abdomen/Training/img/img0002.nii.gz 01_BCV-Abdomen/Training/label/label0002.nii.gz 01_BCV-Abdomen/Training/img/img0003.nii.gz 01_BCV-Abdomen/Training/label/label0003.nii.gz ``` ### cache 3d data into slices After generating the ```[example]_train.txt``` file, check the config file ```configs/vit_b.yaml```. Update the params in ```dataset``` by yours. And the ```dataset_list``` should be the name of the generated txt file ```[example]```. Then run ``` python -m datasets.cache_dataset3d ``` ## Training run training ``` CUDA_VISIBLE_DEVICES=0,1,2,3 python -m core.ddp --tag debug ``` ## Testing ``` python -m core.volume_predictor ```