Download
A lightweight vision library for performing large-scale object detection & instance segmentation. Object detection and instance segmentation are by far the most important fields of applications in Computer Vision. However, detection of small objects and inference on large images are still major issues in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities. Detection of small objects and objects far away in the scene is a major challenge in surveillance applications. Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult to detect using conventional detectors. In this work, an open-source framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection.
Features
- Perform sliced/standard video/image prediction
- Perform sliced/standard prediction using any yolov5/mmdet/detectron2/huggingface model and explore results in fiftyone app
- Automatically slice COCO annotation and image files
- Explore multiple prediction results on your COCO dataset with fiftyone ui ordered by number of misdetections
- Evaluate classwise COCO AP and AR for given predictions and ground truth
- Calculate and export many error analysis plots
Project Samples
