Add FastSAM docs page (#3371)

docs/models/fast-sam.md

@@ -0,0 +1,104 @@
+---
+comments: true
+description: Explore the Fast Segment Anything Model (FastSAM), a real-time solution for the segment anything task that leverages a Convolutional Neural Network (CNN) for segmenting any object within an image, guided by user interaction prompts.
+keywords: FastSAM, Segment Anything Model, SAM, Convolutional Neural Network, CNN, image segmentation, real-time image processing
+---
+
+# Fast Segment Anything Model (FastSAM)
+
+The Fast Segment Anything Model (FastSAM) is a novel, real-time CNN-based solution for the Segment Anything task. This task is designed to segment any object within an image based on various possible user interaction prompts. FastSAM significantly reduces computational demands while maintaining competitive performance, making it a practical choice for a variety of vision tasks.
+
+![Fast Segment Anything Model (FastSAM) architecture overview](https://user-images.githubusercontent.com/26833433/248551984-d98f0f6d-7535-45d0-b380-2e1440b52ad7.jpg)
+
+## Overview
+
+FastSAM is designed to address the limitations of the Segment Anything Model (SAM), a heavy Transformer model with substantial computational resource requirements. The FastSAM decouples the segment anything task into two sequential stages: all-instance segmentation and prompt-guided selection. The first stage uses a Convolutional Neural Network (CNN)-based detector to produce the segmentation masks of all instances in the image. In the second stage, it outputs the region-of-interest corresponding to the prompt.
+
+## Key Features
+
+1. **Real-time Solution:** By leveraging the computational efficiency of CNNs, FastSAM provides a real-time solution for the segment anything task, making it valuable for industrial applications that require quick results.
+
+2. **Efficiency and Performance:** FastSAM offers a significant reduction in computational and resource demands without compromising on performance quality. It achieves comparable performance to SAM but with drastically reduced computational resources, enabling real-time application.
+
+3. **Prompt-guided Segmentation:** FastSAM can segment any object within an image guided by various possible user interaction prompts, providing flexibility and adaptability in different scenarios.
+
+4. **Based on YOLOv8-seg:** FastSAM is based on YOLOv8-seg, an object detector equipped with an instance segmentation branch. This allows it to effectively produce the segmentation masks of all instances in an image.
+
+5. **Competitive Results on Benchmarks:** On the object proposal task on MS COCO, FastSAM achieves high scores at a significantly faster speed than SAM on a single NVIDIA RTX 3090, demonstrating its efficiency and capability.
+
+6. **Practical Applications:** The proposed approach provides a new, practical solution for a large number of vision tasks at a really high speed, tens or hundreds of times faster than current methods.
+
+7. **Model Compression Feasibility:** FastSAM demonstrates the feasibility of a path that can significantly reduce the computational effort by introducing an artificial prior to the structure, thus opening new possibilities for large model architecture for general vision tasks.
+
+## Usage
+
+FastSAM is not yet available directly via the `ultralytics` package, but it is available directly from the [https://github.com/CASIA-IVA-Lab/FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) repository. Here is a brief overview of the typical steps you might take to use FastSAM:
+
+### Installation
+
+1. Clone the FastSAM repository:
+   ```shell
+   git clone https://github.com/CASIA-IVA-Lab/FastSAM.git
+   ```
+
+2. Create and activate a Conda environment with Python 3.9:
+   ```shell
+   conda create -n FastSAM python=3.9
+   conda activate FastSAM
+   ```
+
+3. Navigate to the cloned repository and install the required packages:
+   ```shell
+   cd FastSAM
+   pip install -r requirements.txt
+   ```
+
+4. Install the CLIP model:
+   ```shell
+   pip install git+https://github.com/openai/CLIP.git
+   ```
+
+### Example Usage
+
+1. Download a [model checkpoint](https://drive.google.com/file/d/1m1sjY4ihXBU1fZXdQ-Xdj-mDltW-2Rqv/view?usp=sharing).
+
+2. Use FastSAM for inference. Example commands:
+
+    - Segment everything in an image:
+      ```shell
+      python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg
+      ```
+
+    - Segment specific objects using text prompt:
+      ```shell
+      python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --text_prompt "the yellow dog"
+      ```
+
+    - Segment objects within a bounding box (provide box coordinates in xywh format):
+      ```shell
+      python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --box_prompt "[570,200,230,400]"
+      ```
+
+    - Segment objects near specific points:
+      ```shell
+      python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --point_prompt "[[520,360],[620,300]]" --point_label "[1,0]"
+      ```
+
+Additionally, you can try FastSAM through a [Colab demo](https://colab.research.google.com/drive/1oX14f6IneGGw612WgVlAiy91UHwFAvr9?usp=sharing) or on the [HuggingFace web demo](https://huggingface.co/spaces/An-619/FastSAM) for a visual experience.
+
+## Citations and Acknowledgements
+
+We would like to acknowledge the FastSAM authors for their significant contributions in the field of real-time instance segmentation:
+
+```bibtex
+@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}
+}
+```
+
+The original FastSAM paper can be found on [arXiv](https://arxiv.org/abs/2306.12156). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/CASIA-IVA-Lab/FastSAM). We appreciate their efforts in advancing the field and making their work accessible to the broader community.

docs/models/index.md

@@ -17,8 +17,9 @@ In this documentation, we provide information on four major models:
 5. [YOLOv7](./yolov7.md): Updated YOLO models released in 2022 by the authors of YOLOv4.
 6. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
 7. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
-8. [YOLO-NAS](./yolo-nas.md): YOLO Neural Architecture Search (NAS) Models.
+8. [Fast Segment Anything Model (FastSAM)](./fast-sam.md): FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
-9. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
+9. [YOLO-NAS](./yolo-nas.md): YOLO Neural Architecture Search (NAS) Models.
+10. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
 
 You can use many of these models directly in the Command Line Interface (CLI) or in a Python environment. Below are examples of how to use the models with CLI and Python:
 

mkdocs.yml

@@ -168,6 +168,7 @@ nav:
       - YOLOv7: models/yolov7.md
       - YOLOv8: models/yolov8.md
       - SAM (Segment Anything Model): models/sam.md
+      - FastSAM (Fast Segment Anything Model): models/fast-sam.md
       - YOLO-NAS (Neural Architecture Search): models/yolo-nas.md
       - RT-DETR (Realtime Detection Transformer): models/rtdetr.md
   - Datasets: