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.

Python library 1.2.6 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==1.2.6

🚀MAIN UPDATES:
Increased the processing speed of the visualize_results_usual_yolo_inference function for the task of instance segmentation visualization, and added the ability to pass extra arguments to the inference. You can find a list of possible additional arguments in the Ultralytics documentation - here.

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

Python library 1.2.4 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==1.2.4

🚀MAIN UPDATES:
For the task of instance segmentation, we have managed to significantly speed up the inference process and resolve the issue of excessive memory load (thanks to the transition from storing binary masks of objects to storing the coordinates of approximated polygons).

Python library 1.1.2 version

This library facilitates various visualizations of inference results from ultralytics segmentation/detection models, including cropping with overlays, as well as a patch-based inference algorithm enabling the detection of small objects in images. It caters to both object detection and instance segmentation tasks.

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==1.1.2

Python library 1.1.0 version

This library facilitates various visualizations of inference results from ultralytics segmentation/detection models, including cropping with overlays, as well as a patch-based inference algorithm enabling the detection of small objects in images. It caters to both object detection and instance segmentation tasks.

Model Support: The library offers support for multiple ultralytics deep learning models, such as YOLOv8, YOLOv9, SAM, and RTDETR. Users can select from pre-trained options or utilize custom-trained models to best meet their task requirements.

Functional Python library 1.0.2 version

A fully functional Python library, implemented solely through the utilization of the Non-Maximum Suppression (NMS) algorithm based on bounding boxes from model inferences, to suppress duplicates arising from overlapping patches.