The official code for BA-SAM. This method is elaborated in the paper BA-SAM.
conda env create -f environment.yml
Download SAM checkpoint, and put it at ./checkpoint/sam/
Donwload MobileSAM, and put it at ./checkpoint/
- Download ISIC dataset part 1 from https://challenge.isic-archive.com/data/. Then put the csv files in "./data/ISIC" under your data path. Your dataset folder under "your_data_path" should be like:
ISIC/
ISBI2016_ISIC_Part1_Test_Data/...
ISBI2016_ISIC_Part1_Training_Data/...
ISBI2016_ISIC_Part1_Test_GroundTruth.csv
ISBI2016_ISIC_Part1_Training_GroundTruth.csv
-
Begin Adapting! run:
python train.py -net sam -mod sam_adpt -exp_name *msa_test_isic* -sam_ckpt ./checkpoint/sam/sam_vit_b_01ec64.pth -image_size 1024 -b 32 -dataset isic --data_path *../data*change "data_path" and "exp_name" for your own useage. -
Evaluation: The code can automatically evaluate the model on the test set during traing, set "--val_freq" to control how many epoches you want to evaluate once. You can also run val.py for the independent evaluation. We provide several ckpt here: ckpt
-
Result Visualization: You can set "--vis" parameter to control how many epoches you want to see the results in the training or evaluation process.
In default, everything will be saved at ./logs/
It is simple to run BA-SAM on the other datasets. Just write another dataset class following which in ./dataset.py. You only need to make sure you return a dict with
{
'image': A tensor saving images with size [C,H,W] for 2D image, size [C, H, W, D] for 3D data.
D is the depth of 3D volume, C is the channel of a scan/frame, which is commonly 1 for CT, MRI, US data.
If processing, say like a colorful surgical video, D could the number of time frames, and C will be 3 for a RGB frame.
'label': The target masks. Same size with the images except the resolutions (H and W).
'p_label': The prompt label to decide positive/negative prompt. To simplify, you can always set 1 if don't need the negative prompt function.
'pt': The prompt. Should be the same as that in SAM, e.g., a click prompt should be [x of click, y of click], one click for each scan/frame if using 3d data.
'image_meta_dict': Optional. if you want save/visulize the result, you should put the name of the image in it with the key ['filename_or_obj'].
...(others as you want)
}
Welcome to open issues if you meet any problem. It would be appreciated if you could contribute your dataset extensions. Unlike natural images, medical images vary a lot depending on different tasks. Expanding the generalization of a method requires everyone's efforts. Our code is build upon MSA
- Release base code and ckpt.
- Fix bugs
- More details
If you use BA-SAM in your research, please use the following BibTeX entry.
@article{song2024ba,
title={BA-SAM: Scalable Bias-Mode Attention Mask for Segment Anything Model},
author={Song, Yiran and Zhou, Qianyu and Li, Xiangtai and Fan, Deng-Ping and Lu, Xuequan and Ma, Lizhuang},
journal={arXiv preprint arXiv:2401.02317},
year={2024}
}
@article{kirillov2023segany,
title={Segment Anything},
author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
journal={arXiv:2304.02643},
year={2023}
}
@article{mobile_sam,
title={Faster Segment Anything: Towards Lightweight SAM for Mobile Applications},
author={Zhang, Chaoning and Han, Dongshen and Qiao, Yu and Kim, Jung Uk and Bae, Sung-Ho and Lee, Seungkyu and Hong, Choong Seon},
journal={arXiv preprint arXiv:2306.14289},
year={2023}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent