Fast Segment Anything

[📕Paper] [🤗HuggingFace Demo] [Colab demo] [Replicate demo & API] [OpenXLab Demo] [Model Zoo] [BibTeX] [Video Demo]

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

🍇 Updates

• 2024/6/25 The edge jaggies issue has been slightly improved #231, and the strategy has also been synchronized to the ultralytics project#13939,#13912. The huggingface demo is updated.
• 2023/11/28 Recommendation: Semantic FastSAM, which add the semantic class labels to FastSAM. Thanks to KBH00 for this valuable contribution.
• 2023/09/11 Release Training and Validation Code.
• 2023/08/17 Release OpenXLab Demo. Thanks to OpenXLab Team for help.
• 2023/07/06 Added to Ultralytics (YOLOv8) Model Hub. Thanks to Ultralytics for help 🌹.
• 2023/06/29 Support text mode in HuggingFace Space. Thanks a lot to gaoxinge for help 🌹.
• 2023/06/29 Release FastSAM_Awesome_TensorRT. Thanks a lot to ChuRuaNh0 for providing the TensorRT model of FastSAM 🌹.
• 2023/06/26 Release FastSAM Replicate Online Demo. Thanks a lot to Chenxi for providing this nice demo 🌹.
• 2023/06/26 Support points mode in HuggingFace Space. Better and faster interaction will come soon!
• 2023/06/24 Thanks a lot to Grounding-SAM for Combining Grounding-DINO with FastSAM in Grounded-FastSAM 🌹.

Installation

Clone the repository locally:

git clone https://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(Required if the text prompt is being tested.):

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 (xywh)
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]"

You can use the following code to generate all masks and visualize the results.

from fastsam import FastSAM, FastSAMPrompt

model = FastSAM('./weights/FastSAM.pt')
IMAGE_PATH = './images/dogs.jpg'
DEVICE = 'cpu'
everything_results = model(IMAGE_PATH, device=DEVICE, retina_masks=True, imgsz=1024, conf=0.4, iou=0.9,)
prompt_process = FastSAMPrompt(IMAGE_PATH, everything_results, device=DEVICE)

# everything prompt
ann = prompt_process.everything_prompt()

prompt_process.plot(annotations=ann,output_path='./output/dog.jpg',)

For point/box/text mode prompts, use:

# bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
ann = prompt_process.box_prompt(bboxes=[[200, 200, 300, 300]])

# text prompt
ann = prompt_process.text_prompt(text='a photo of a dog')

# point prompt
# points default [[0,0]] [[x1,y1],[x2,y2]]
# point_label default [0] [1,0] 0:background, 1:foreground
ann = prompt_process.point_prompt(points=[[620, 360]], pointlabel=[1])

prompt_process.plot(annotations=ann,output_path='./output/dog.jpg',)

You are also welcomed to try our Colab demo: FastSAM_example.ipynb.

Different Inference Options

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

Training or Validation

Training from scratch or validation: Training and Validation Code.

Web demo

Gradio demo

• We also provide a UI for testing our method that is built with gradio. You can upload a custom image, select the mode and set the parameters, click the segment button, and get a satisfactory segmentation result. Currently, the UI supports interaction with the 'Everything mode' and 'points mode'. We plan to add support for additional modes in the future. Running the following command in a terminal will launch the demo:

# Download the pre-trained model in "./weights/FastSAM.pt"
python app_gradio.py

• This demo is also hosted on HuggingFace Space.

Replicate demo

• Replicate demo has supported all modes, you can experience points/box/text mode.

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 | Baidu Cloud (pwd: 0000).
• 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 Visualization

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.

Contributors

Our project wouldn't be possible without the contributions of these amazing people! Thank you all for making this project better.

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}
}