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an empirical study on few-shot counting using segment anything (SAM)

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# count-anything
An empirical study on few-shot counting using segment anything (SAM)
 
[Online Demo](https://huggingface.co/spaces/nebula/counting-anything)

[arXiv](http://arxiv.org/abs/2304.10817)

Meta AI recently released the Segment Anything model [[SAM]](https://github.com/facebookresearch/segment-anything), which has garnered attention due to its impressive performance in class-agnostic segmenting. In this study, we explore the use of SAM for the challenging task of few-shot object counting, which involves counting objects of an unseen category by providing a few bounding boxes of examples. We compare SAM's performance with other few-shot counting methods and find that it is currently unsatisfactory without further fine-tuning, particularly for small and crowded objects.

![image](https://raw.githubusercontent.com/Vision-Intelligence-and-Robots-Group/count-anything/main/example.png)
## Install
Install python dependencies. We use conda and python 3.10.4 and PyTorch 1.13.1
> conda env create -f env.yaml

## Dataset preparation
- For FSC-147:
    Images can be downloaded from here: https://drive.google.com/file/d/1ymDYrGs9DSRicfZbSCDiOu0ikGDh5k6S/view?usp=sharing

- For COCO val2017:
    Images can be downloaded from here: https://cocodataset.org/
## Comparison Results

### FSC

![image](https://raw.githubusercontent.com/Vision-Intelligence-and-Robots-Group/count-anything/main/resultFSC.png)

### COCO

![image](https://raw.githubusercontent.com/Vision-Intelligence-and-Robots-Group/count-anything/main/resultcoco.png)
## Test
Download the [ViT-H SAM model](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth)

- For FSC-147:
```
python test_FSC.py --data_path <FSC-147 dataset path> --model_path <path to ViT-H SAM model>
```

- For COCO val2017:
```
python test_coco.py --data_path <coco val2017 dataset path\> --model_path <path to ViT-H SAM model>
```

## Visualize
You can run [vis_FSC.ipynb](vis_FSC.ipynb) for FSC-147 or [vis_coco.ipynb](vis_coco.ipynb) for coco.

## Acknowledgement
We thank facebookresearch for their segment-anything model [[project]](https://github.com/facebookresearch/segment-anything), cvlab-stonybrook for their Learning To Count Everything [[project]](https://github.com/cvlab-stonybrook/LearningToCountEverything) and coco [[datasets]](https://cocodataset.org/).

## Citation
If you find the code useful, please cite:
```
@article{ma2023countanything,
  title={CAN SAM COUNT ANYTHING? AN EMPIRICAL STUDY ON SAM COUNTING},
  author={Ma, Zhiheng and Hong, Xiaopeng and Shangguan Qinnan},
  journal={arXiv preprint arXiv:2304.10817},
  year={2023}
}
```

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