Segment Anything Is Not Always Perfect

Code repository for our paper titled "Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-World Applications" (CVPRW Oral).

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Updates

• Another work, Medical SAM Adapter which addresses the issue of lacking domain-specific medical knowledge in the SAM, are available now.
• Long version of this work has been accepted by Machine Intelligence Research.
• This work is awarded as Best Paper (Most Insightful Paper) at the CVPR'23 VISION Workshop.
• Evaluation code has been released.
• This work has been accepted as an Oral Presentation at the CVPR'23 VISION Workshop.

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Get Started

Eval SAM in different dataset

1. Download the vit_b, vit_h and vim_l model from https://github.com/facebookresearch/segment-anything then put these models to the model_ck folder.
2. Prepared own datasets put into the datasets folder.
3. Set right path in /scripts/amg.py, then:

run amg.py

Chosen best results form the sam_output folder

1. After inferring, the SAM model generates predicted maps from a singer RGB image (multimask_output=True). Check right path in sam_dice_f1_mae.py or sam_f1_dice_mae.py to decide the best map selected by Dice or F1 metrics.

Eval other methods in different dataset

1. Prepared these methods predicted maps to put into the other_methods_output folder.
2. Check right path in /scripts/other_methods_dice_mae.py, then:

run other_methods_dice_mae.py

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Datasets

The download links of the dataset involved in our work are provided below.

DUTS	COME15K	VT1000	DIS	COD10K	SBU	CDS2K	ColonDB
Link	Link	Link	Link	Link	Link	Link	Link

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Citation

If you find our work useful for your research or applications, please cite using this BibTeX:

@misc{ji2023segment,
      title={Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications}, 
      author={Wei Ji and Jingjing Li and Qi Bi and Tingwei Liu and Wenbo Li and Li Cheng},
      year={2023},
      eprint={2304.05750},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{wu2023medical,
      title={Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation}, 
      author={Junde Wu and Wei Ji and Yuanpei Liu and Huazhu Fu and Min Xu and Yanwu Xu and Yueming Jin},
      year={2023},
      eprint={2304.12620},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgement

Thanks for the efforts of the authors involved in the Segment Anything.