Fast Segment Anything

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The Fast Segment Anything Model(FastSAM) is a CNN Segment Anything Model trained by only 2% of the SA-1B dataset published by SAM authors. The FastSAM achieve a comparable performance with the SAM method at 50× higher run-time speed.

Installation

Clone the repository locally:

git clone git@github.com:CASIA-IVA-Lab/FastSAM.git

Create the conda env. The code requires python>=3.7, as well as pytorch>=1.7 and torchvision>=0.8. Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.

conda create -n FastSAM python=3.9
conda activate FastSAM

Install the packages:

cd FastSAM
pip install -r requirements.txt

Install clip:

pip install git+https://github.com/openai/CLIP.git

Getting Started

First download a model checkpoint.

Then, you can run the scripts to try the everything mode and three prompt modes.

# Everything mode
python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg

# text prompt
python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg  --text_prompt "the yellow dog"

# box prompt
python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --box_prompt [570,200,230,400]

# points prompt
python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg  --point_prompt "[[520,360],[620,300]]" --point_label "[1,0]"

Different Inference Options

We provide various options for different purposes, details are in MORE_USAGES.md.

Web demo

In the web demo, you can upload your own image, select input size from 512~1024, and choose whether to visualize in high quality. High quality visualization additionally shows more easily observable split edges. The web demo only supports Everything Mode now, other modes will try to support in the future.

Model Checkpoints

Two model versions of the model are available with different sizes. Click the links below to download the checkpoint for the corresponding model type.

default or FastSAM: YOLOv8x based Segment Anything Model.
FastSAM-s: YOLOv8s based Segment Anything Model.

Results

All result were tested on a single NVIDIA GeForce RTX 3090.

1. Inference time

Running Speed under Different Point Prompt Numbers(ms).

method	params	1	10	100	E(16x16)	E(32x32*)	E(64x64)
SAM-H	0.6G	446	464	627	852	2099	6972
SAM-B	136M	110	125	230	432	1383	5417
FastSAM	68M	40	40	40	40	40	40

2. Memory usage

Dataset	Method	GPU Memory (MB)
COCO 2017	FastSAM	2608
COCO 2017	SAM-H	7060
COCO 2017	SAM-B	4670

3. Zero-shot Transfer Experiments

Edge Detection

Test on the BSDB500 dataset.

method	year	ODS	OIS	AP	R50
HED	2015	.788	.808	.840	.923
SAM	2023	.768	.786	.794	.928
FastSAM	2023	.750	.790	.793	.903

Object Proposals

COCO

method	AR10	AR100	AR1000	AUC
SAM-H E64	15.5	45.6	67.7	32.1
SAM-H E32	18.5	49.5	62.5	33.7
SAM-B E32	11.4	39.6	59.1	27.3
FastSAM	15.7	47.3	63.7	32.2

LVIS

bbox AR@1000

method	all	small	med.	large
ViTDet-H	65.0	53.2	83.3	91.2
zero-shot transfer methods
SAM-H E64	52.1	36.6	75.1	88.2
SAM-H E32	50.3	33.1	76.2	89.8
SAM-B E32	45.0	29.3	68.7	80.6
FastSAM	57.1	44.3	77.1	85.3

Instance Segmentation On COCO 2017

method	AP	APS	APM	APL
ViTDet-H	.510	.320	.543	.689
SAM	.465	.308	.510	.617
FastSAM	.379	.239	.434	.500

4. Performance Visulization

Several segmentation results:

Natural Images

Text to Mask

5.Downstream tasks

The results of several downstream tasks to show the effectiveness.

Anomaly Detection

Salient Object Detection

Building Extracting

License

The model is licensed under the Apache 2.0 license.

Acknowledgement

Segment Anything provides the SA-1B dataset and the base codes.
YOLOv8 provides codes and pre-trained models.
YOLACT provides powerful instance segmentation method.
Grounded-Segment-Anything provides a useful web demo template.

Citing FastSAM

If you find this project useful for your research, please consider citing the following BibTeX entry.

@misc{zhao2023fast,
      title={Fast Segment Anything}, 
      author={Xu Zhao and Wenchao Ding and Yongqi An and Yinglong Du and Tao Yu and Min Li and Ming Tang and Jinqiao Wang},
      year={2023},
      eprint={2306.12156},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Fictiverse/FastSAM