Python library 1.2.9 version

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, 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==1.2.9

🚀MAIN UPDATES:
The capability to convert a list of polygons obtained from inference into a list of binary arrays using the function create_masks_from_polygons has been added. Additionally, the ability to manually set display colors for classes in the visualization function has been introduced; previously, one could only choose to randomly generate colors for the classes. Furthermore, the algorithm has been optimized in the mask processing mode with memory_optimize=False, which has reduced memory consumption in this mode by approximately half.