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==1.3.0
🚀MAIN UPDATES:
There is now an option to choose between class-agnostic and not class-agnostic types of NMS, which improves quality in the case of a multi-class detection or instance segmentation task.
To efficiently process a large number of images of varying sizes and contents, manually selecting the optimal patch sizes and overlaps can be difficult. To address this, an algorithm auto_calculate_crop_values has been developed to automatically calculate the best parameters for patches (crops).
Furthermore, enhanced default parameters for more effective patched inference have been established.