Python library 1.3.7 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, 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==1.3.7

🚀MAIN UPDATES:
Added the ability to retrieve information about how many objects of each class were detected on each of the obtained patches during inference. This can be done by calling the patches_info() function on an instance of the MakeCropsDetectThem class.

Additionally, the new update introduces a convenient function for visualizing the results of standard inference for YOLO-pose neural networks (yolov8-pose & yolo11-pose). You can see an example of how to use this function by visiting this Colab notebook -