This Python library simplifies SAHI-like inference for instance segmentation tasks, enabling the detection of small objects in images. It caters to both object detection and instance segmentation tasks, supporting a wide range of Ultralytics models.
The library also provides a sleek customization of the visualization of the inference results for all models, both in the standard approach (direct network run) and the unique patch-based variant.
Model Support: The library offers support for multiple ultralytics deep learning models, such as YOLOv8, YOLOv8-seg, YOLOv9, YOLOv9-seg, YOLO11, YOLO11-seg, FastSAM, and RTDETR. Users can select from pre-trained options or utilize custom-trained models to best meet their task requirements.
pip install patched-yolo-infer==1.3.3
🚀MAIN UPDATES:
There is an opportunity to produce cropping into patches at different resolutions. This way, small objects can be detected when cropping into smaller patches, and large objects can be detected when cropping into larger patches. As a result, the algorithm will be able to detect a wider range of object sizes in the frame. To achieve this, the image needs to be processed multiple times through MakeCropsDetectThem with different patch parameters, and then pass the list of element_crops to the CombineDetections process. Below is an example of this approach:
An example of using this approach can be seen in this Google Colab notebook –
