GitHub

Fast Segment Anything

What is the project about?

Fast Segment Anything Model (FastSAM) is a CNN-based model for image segmentation. It's designed to be a fast alternative to the original Segment Anything Model (SAM).

What problem does it solve?

FastSAM addresses the computational cost and speed limitations of the original SAM model. It provides a much faster solution for image segmentation while maintaining comparable performance. It aims for real-time or near real-time segmentation.

What are the features of the project?

  • High-speed segmentation: Achieves significantly higher runtime speeds (up to 50x) compared to SAM.
  • Multiple prompt modes: Supports various input prompts:
    • Everything mode: Automatically segments all objects in an image.
    • Text prompt: Segments objects based on a text description.
    • Box prompt: Segments objects within a specified bounding box.
    • Points prompt: Segments objects based on user-provided points (foreground/background).
  • Comparable performance: While much faster, it achieves performance close to the original SAM on various tasks.
  • Zero-shot transfer: Demonstrates good performance on tasks it wasn't explicitly trained for, like edge detection and object proposal generation.
  • Integration with other tools: Easily integrates with tools like Ultralytics (YOLOv8) and Grounding DINO.
  • Interactive demos: Provides multiple interactive demos (HuggingFace, Colab, Replicate) for easy experimentation.
  • Edge Jaggies improvement: Slight improvement on the edge jaggies.

What are the technologies used in the project?

  • Python: The primary programming language.
  • PyTorch: The deep learning framework used for model development and training.
  • TorchVision: Used for image processing and computer vision tasks.
  • YOLOv8: The underlying object detection architecture (specifically, YOLOv8x and YOLOv8s variants).
  • CNNs (Convolutional Neural Networks): The core deep learning model type.
  • CLIP (optional): Used for text prompt functionality.
  • Gradio: Used for building the web UI demo.
  • TensorRT (optional): For optimized inference (provided by a third-party contributor).

What are the benefits of the project?

  • Speed: Enables real-time or near real-time image segmentation applications.
  • Efficiency: Reduces computational cost and resource requirements compared to SAM.
  • Accessibility: Offers user-friendly demos and easy-to-use code.
  • Versatility: Applicable to a wide range of image segmentation tasks.
  • Open-source: Freely available under the Apache 2.0 license.

What are the use cases of the project?

  • Image editing: Quickly selecting and modifying objects in images.
  • Object detection and tracking: As a component in larger computer vision systems.
  • Robotics: For scene understanding and object manipulation.
  • Autonomous driving: Segmenting objects in the environment.
  • Medical imaging: Analyzing and segmenting anatomical structures.
  • Anomaly detection: Identifying unusual patterns in images.
  • Salient object detection: Highlighting the most important objects in a scene.
  • Building extraction: Identifying and outlining buildings in aerial or satellite imagery.
  • Video processing: Real-time segmentation in video streams.
  • Any application requiring fast and accurate image segmentation.
FastSAM screenshot