Python library 1.2.5 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==1.2.5

🚀MAIN UPDATES:
Possibility to pass extra arguments to the inference function was added
You can find a list of possible additional arguments in the Ultralytics documentation here.

Code example:

element_crops = MakeCropsDetectThem(
    image=img,
    model_path="yolov8m-seg.pt",
    segment=True,
    inference_extra_args={
            'retina_masks': True
        }
)

This feature will allow you to customize patched_yolo_infer even better for your project