sahi

SAHI: Slicing Aided Hyper Inference

Overview

SAHI, which stands for Slicing Aided Hyper Inference, is a streamlined vision library designed to facilitate large-scale object detection and instance segmentation. This tool focuses on solving practical challenges, particularly enhancing the detection of small objects and performing efficient inference on large images. SAHI equips developers with several utilities to tackle real-world issues in computer vision applications.

Key Features

• Object Detection and Instance Segmentation: SAHI fundamentally improves small object detection and handles large images with ease, making it valuable in various computer vision tasks.
• Versatile Command Support: It offers multiple commands like predict, coco slice, and coco evaluate to manage image predictions and evaluations across different models and datasets.
• Framework Agnosticism: The library supports framework-agnostic prediction, allowing it to work across multiple major detection models, such as those from Ultralytics, MMDetection, Detectron2, and more.
• Interactive Tools: Integrated with applications like FiftyOne, SAHI provides interactive result exploration and analysis, improving the understanding and debugging of prediction results.

Getting Started

To use SAHI, installation is straightforward via pip with additional requirements for certain operating systems or frameworks. For instance, installing the library on Windows requires Conda for some dependencies like Shapely. Further, it supports the integration with various deep learning frameworks, notably YOLOv5, Ultralytics, MMDetection, and more.

pip install sahi
conda install -c conda-forge shapely

For framework-specific installations like detectron2 or mmdet, detailed console commands ensure users can seamlessly set up their environment based on their project needs.

Tutorials and Resources

SAHI provides a wealth of tutorials and resources to help users maximize the utility of the library, including:

• Interactive Notebooks: Users can access practical walkthroughs using popular models like YOLOX, YOLOv8, and others. These are available on platforms like Kaggle and Hugging Face Spaces, allowing users to see SAHI in action.
• Detailed Documentation: Comprehensive guides such as error analysis and interactive result visualization enable users to dive deeper into analysis and improve their models' accuracy.
• Community and Collaboration: With a growing list of publications citing SAHI and numerous competition accolades, the library has a robust community of contributors and users sharing insights and improvements.

Innovative Capabilities

SAHI handles tasks such as automatically slicing COCO annotation files and analyzing prediction errors. Moreover, it provides conversion utilities to ease the integration with other formats and models, which is pivotal for tasks requiring collaboration across varied technological stacks.

Contribution and Development

Developers are encouraged to contribute to the evolution of SAHI. Adding new detection frameworks is streamlined, fostering a collaborative and innovative environment for continual enhancement. The library supports a broad range of object detection models, making it flexible and adaptable to new developments in the field.

Conclusion

SAHI stands out as a lightweight yet powerful vision library that addresses specific challenges in large-scale object detection and segmentation. Its framework-agnostic design, coupled with extensive tools and community support, makes it an essential asset for developers and researchers in computer vision. Whether for academic research or industry applications, SAHI empowers users to achieve detailed and efficient inference results.