modify readme

baselines/README.md

@@ -5,9 +5,11 @@ python train.py --data kits -m swin_unetr --snapshot_path "path/to/snapshot/" --
 ```
 Using `-m` to indicate the method. We compared with [nnUNet](https://arxiv.org/abs/1809.10486), [TransBTS](https://arxiv.org/abs/2103.04430), [nnFormer](https://arxiv.org/abs/2109.03201), [Swin UNETR](https://arxiv.org/abs/2111.14791), [UNETR++](https://arxiv.org/abs/2212.04497), and [3D UX-Net](https://arxiv.org/abs/2209.15076). 
 
+To reproduce nnUNet results, please follow the instruction [here](https://github.com/MIC-DKFZ/nnUNet/tree/nnunetv1) on our dataset split. We also provide the pretrained nnUNet checkpoints on 4 datasets [here](https://mycuhk-my.sharepoint.com/:f:/g/personal/1155187957_link_cuhk_edu_hk/EhdwEGpxNJRAn3cNfMF-8UMBE_o2Plt2A1XbwJl5-OdrAA?e=CkmTcH).
+
 For training Swin-UNETR, download the [checkpoint](https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/model_swinvit.pt) and put it under the folder ckpt.
 
-We use various hyper-parameters for each dataset, for more details, please refer to [datasets.py](dataset/datasets.py). The crop size is set as `(64, 160, 160)` for all datasets.
+We use various hyper-parameters for each dataset, for more details, please refer to [datasets.py](dataset/datasets.py). The crop size is set as `(64, 160, 160)` for all datasets. We also provide the pretrained parameters of baseline methods on 4 datasets [here](https://mycuhk-my.sharepoint.com/:f:/g/personal/1155187957_link_cuhk_edu_hk/Ej1hIJtw_vZJmFbxrxHbeEYBy-SnNebi-hdpfvUKFT2Adg?e=8UgQDJ).
 
 # Evaluation
 Type the command below to evaluate the performance baselines: