Python library 1.2.7 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, 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==1.2.7

🚀MAIN UPDATES:

The core advancement in the library update is the implementation of batch inference support coupled with TensorRT technology, significantly enhancing processing speed.
By enabling batch_inference=True during the initialization of the MakeCropsDetectThem class, fps will be improved by approximately 1.5 times. Furthermore, the library now supports any converted ultralytics detection and instance segmentation model in TensorRT format, offering an additional 1.5 times fps increase. This dual-enhancement significantly accelerates video stream processing.