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YOLO Module API Manual

Overview

This manual is intended to guide developers in using the YOLO module to deploy and run models trained and converted with YOLOv5, YOLOv8, and YOLO11. The models support three types of tasks: classification, detection, and segmentation. It helps users quickly interface with the YOLO source code and deploy the trained models on the K230. For YOLO usage examples, refer to the document: yolo_battle

API Introduction

YOLOv5 Class

Constructor

Description

The encapsulated YOLOv5 module constructor, initializes the YOLOv5 type to obtain a YOLOv5 instance.

Syntax

from libs.YOLO import YOLOv5

yolo=YOLOv5(task_type="classify",mode="image",kmodel_path="yolov5_det.kmodel",labels=["apple","banana","orange"],rgb888p_size=[1280,720],model_input_size=[320,320],conf_thresh=0.5,nms_thresh=0.25,max_boxes_num=50,debug_mode=0)

Parameters

Parameter Name	Description	Notes	Type
task_type	Task type	Supports three task types, options are ‘classify’/’detect’/’segment’;	str
mode	Inference mode	Supports two inference modes, options are ‘image’/’video’, ‘image’ means inferring an image, ‘video’ means inferring a real-time video stream captured by the camera;	str
kmodel_path	kmodel path	Path of the kmodel copied to the development board;	str
labels	Class label list	Label names for different categories;	list[str]
rgb888p_size	Inference frame resolution	Resolution of the current inference frame, such as [1920,1080], [1280,720], [640,640];	list[int]
model_input_size	Model input resolution	Input resolution used when training the YOLOv5 model, such as [224,224], [320,320], [640,640];	list[int]
display_size	Display resolution	Set when the inference mode is ‘video’, supports hdmi([1920,1080]) and lcd([800,480]);	list[int]
conf_thresh	Confidence threshold	Class confidence threshold for classification tasks, object confidence threshold for detection and segmentation tasks, e.g., 0.5;	float [0~1]
nms_thresh	nms threshold	Non-maximum suppression threshold, required for detection and segmentation tasks;	float [0~1]
mask_thresh	mask threshold	Binarization threshold for segmenting objects in the detection box during segmentation tasks;	float [0~1]
max_boxes_num	Maximum number of detection boxes	Maximum number of detection boxes allowed to be returned in a single image frame;	int
debug_mode	Debug mode	Whether the timing function is enabled, options 0/1, 0 means no timing, 1 means timing;	int [0/1]

Return Value

Return Value	Description
YOLOv5	YOLOv5 instance

config_preprocess

Description

YOLOv5 preprocessing configuration function.

Syntax

yolo.config_preprocess()

Parameters

Parameter Name	Description	Input / Output	Notes
None

Return Value

Return Value	Description
None

run

Description

Infer one frame of image, return the inference result for use by the draw_result method. The classification task returns the class index and score, the detection task returns a list of detection box positions, scores, and class indices, and the segmentation task returns the mask result and a list of detection box positions, scores, and class indices.

Syntax

res=yolo.run(img)

Parameters

Parameter Name	Description	Input / Output	Notes
img	The image to be inferred in ulab.numpy.ndarray format, or a frame of image obtained from the video stream through get_frame	Input

Return Value

Return Value	Description
res	Model post-processing result, the return value differs for different tasks. The classification task returns the class index and score, the detection task returns a list of detection box positions, scores, and class indices, and the segmentation task returns the mask result and a list of detection box positions, scores, and class indices.

draw_result

Description

Draws the YOLOv5 inference result on the screen or image.

Syntax

yolo.draw_result(res,img_ori)

Parameters

Parameter Name	Description	Input / Output	Notes
res	Inference result of YOLOv5	Input
img_ori	Image instance to be drawn	Input	From read_img or pl.osd_img

Return Value

Return Value	Description
None

Example Program

The following shows an example program for the YOLOv5 detection task:

from libs.YOLO import YOLOv5
from libs.Utils import *
import os,sys,gc
import ulab.numpy as np
import image

if __name__=="__main__":
    # This is only an example. For custom scenarios, please modify it to your own test image, model path, label name, and model input size
    img_path="/sdcard/examples/utils/test_fruit.jpg"
    kmodel_path="/sdcard/examples/kmodel/fruit_det_yolov5n_320.kmodel"
    labels = ["apple","banana","orange"]
    model_input_size=[320,320]

    confidence_threshold = 0.5
    nms_threshold=0.45
    img,img_ori=read_image(img_path)
    rgb888p_size=[img.shape[2],img.shape[1]]
    # Initialize YOLOv5 instance
    yolo=YOLOv5(task_type="detect",mode="image",kmodel_path=kmodel_path,labels=labels,rgb888p_size=rgb888p_size,model_input_size=model_input_size,conf_thresh=confidence_threshold,nms_thresh=nms_threshold,max_boxes_num=50,debug_mode=0)
    yolo.config_preprocess()
    res=yolo.run(img)
    yolo.draw_result(res,img_ori)
    yolo.deinit()
    gc.collect()

The above code shows the code for performing image inference using YOLOv5.

from libs.PipeLine import PipeLine
from libs.YOLO import YOLOv5
from libs.Utils import *
import os,sys,gc
import ulab.numpy as np
import image

if __name__=="__main__":
    # This is only an example. For custom scenarios, please modify it to your own model path, label name, and model input size
    kmodel_path="/sdcard/examples/kmodel/fruit_det_yolov5n_320.kmodel"
    labels = ["apple","banana","orange"]
    model_input_size=[320,320]

    # Add display mode, default hdmi, options are hdmi/lcd/lt9611/st7701/hx8399, where hdmi defaults to lt9611 with resolution 1920*1080; lcd defaults to st7701 with resolution 800*480
    display_mode="lcd"
    rgb888p_size=[640,360]
    confidence_threshold = 0.8
    nms_threshold=0.45
    pl=PipeLine(rgb888p_size=rgb888p_size,display_mode=display_mode)
    pl.create()
    display_size=pl.get_display_size()
    # Initialize YOLOv5 instance
    yolo=YOLOv5(task_type="detect",mode="video",kmodel_path=kmodel_path,labels=labels,rgb888p_size=rgb888p_size,model_input_size=model_input_size,display_size=display_size,conf_thresh=confidence_threshold,nms_thresh=nms_threshold,max_boxes_num=50,debug_mode=0)
    yolo.config_preprocess()
    while True:
        with ScopedTiming("total",1):
            img=pl.get_frame()
            res=yolo.run(img)
            yolo.draw_result(res,pl.osd_img)
            pl.show_image()
            gc.collect()
    yolo.deinit()
    pl.destroy()

The above code shows the code for performing video inference using YOLOv5.

YOLOv8 Class

Constructor

Description

The encapsulated YOLOv8 module constructor, initializes the YOLOv8 type to obtain a YOLOv8 instance.

Syntax

from libs.YOLO import YOLOv8

yolo=YOLOv8(task_type="classify",mode="image",kmodel_path="yolov8_det.kmodel",labels=["apple","banana","orange"],rgb888p_size=[1280,720],model_input_size=[320,320],conf_thresh=0.5,nms_thresh=0.25,max_boxes_num=50,debug_mode=0)

Parameters

Parameter Name	Description	Description	Type
task_type	Task type	Supports four types of tasks, optional values are ‘classify’/’detect’/’segment’/’obb’;	str
mode	Inference mode	Supports two inference modes, optional values are ‘image’/’video’, ‘image’ means inferring an image, ‘video’ means inferring a real-time video stream captured by the camera;	str
kmodel_path	kmodel path	The path of the kmodel copied to the development board;	str
labels	Class label list	Label names for different classes;	list[str]
rgb888p_size	Inference frame resolution	The resolution of the current inference frame, such as [1920,1080], [1280,720], [640,640];	list[int]
model_input_size	Model input resolution	The input resolution when training the YOLOv8 model, such as [224,224], [320,320], [640,640];	list[int]
display_size	Display resolution	Set when the inference mode is ‘video’, supports hdmi([1920,1080]) and lcd([800,480]);	list[int]
conf_thresh	Confidence threshold	Class confidence threshold for classification tasks, object confidence threshold for detection and segmentation tasks, such as 0.5;	float [0~1]
nms_thresh	nms threshold	Non-maximum suppression threshold, required for detection and segmentation tasks;	float [0~1]
mask_thresh	mask threshold	The binarization threshold for segmenting objects in the detection box in the segmentation task;	float [0~1]
max_boxes_num	Maximum number of detection boxes	The maximum number of detection boxes allowed to be returned in one frame of image;	int
kp_num	Number of keypoints	The number of keypoints in the keypoint detection task;	int
kp_dim	Keypoint dimension	The dimension of keypoints in the keypoint detection task, only 2 and 3 are supported, determined by the trained model;	int【2/3】
debug_mode	Debug mode	Whether the timing function is enabled, optional values are 0/1, 0 means no timing, 1 means timing;	int [0/1]

Return Value

Return Value	Description
YOLOv8	YOLOv8 instance

config_preprocess

Description

YOLOv8 preprocessing configuration function.

Syntax

yolo.config_preprocess()

Parameters

Parameter Name	Description	Input / Output	Description
None

Return Value

Return Value	Description
None

run

Description

Infer one frame of image, return the inference result for use by the draw_result method. The classification task returns the class index and score, the detection task returns a list of detection box positions, scores, and class indices, the segmentation task returns the mask result and a list of detection box positions, scores, and class indices, the rotated object detection returns a list of the four point coordinates of the rotated detection box, and the keypoint detection returns the detection box position, class index, score, and keypoint list.

Syntax

res=yolo.run(img)

Parameters

Parameter Name	Description	Input / Output	Description
img	The image to be inferred in the format of ulab.numpy.ndarray, or a frame of image obtained from the video stream through get_frame	Input

Return Value

Return Value

Description

res

Model post-processing result, the return value varies for different tasks. The classification task returns the class index and score, the detection task returns a list of detection box positions, scores, and class indices, the segmentation task returns the mask result and a list of detection box positions, scores, and class indices, the rotated object detection returns a list of the four point coordinates of the rotated detection box, and the keypoint detection returns the detection box position, class index, score, and keypoint list.

draw_result

Description

Draws the YOLOv8 inference result on the screen or image.

Syntax

yolo.draw_result(res,img_ori)

Parameters

Parameter Name	Description	Input / Output	Description
res	The inference result of YOLOv8	Input
img_ori	The Image instance to be drawn	Input	From read_img or pl.osd_img

Return Value

Return Value	Description
None

Example Program

The following is an example program for the YOLOv8 classification task:

from libs.YOLO import YOLOv8
from libs.Utils import *
import os,sys,gc
import ulab.numpy as np
import image

if __name__=="__main__":
    # This is only an example, please modify it to your own test image, model path, label name, and model input size for custom scenarios
    img_path="/sdcard/examples/utils/test_fruit.jpg"
    kmodel_path="/sdcard/examples/kmodel/fruit_det_yolov8n_320.kmodel"
    labels = ["apple","banana","orange"]
    model_input_size=[320,320]

    confidence_threshold = 0.5
    nms_threshold=0.45
    img,img_ori=read_image(img_path)
    rgb888p_size=[img.shape[2],img.shape[1]]
    # Initialize the YOLOv8 instance
    yolo=YOLOv8(task_type="detect",mode="image",kmodel_path=kmodel_path,labels=labels,rgb888p_size=rgb888p_size,model_input_size=model_input_size,conf_thresh=confidence_threshold,nms_thresh=nms_threshold,max_boxes_num=50,debug_mode=0)
    yolo.config_preprocess()
    res=yolo.run(img)
    yolo.draw_result(res,img_ori)
    yolo.deinit()
    gc.collect()

The above code gives an example of using YOLOv8 for image inference.

from libs.PipeLine import PipeLine
from libs.YOLO import YOLOv8
from libs.Utils import *
import os,sys,gc
import ulab.numpy as np
import image

if __name__=="__main__":
    # This is only an example, please modify it to your own model path, label name, and model input size for custom scenarios
    kmodel_path="/sdcard/examples/kmodel/fruit_det_yolov8n_320.kmodel"
    labels = ["apple","banana","orange"]
    model_input_size=[320,320]

    # Add display mode, default is hdmi, optional hdmi/lcd/lt9611/st7701/hx8399, where hdmi defaults to lt9611, resolution 1920*1080; lcd defaults to st7701, resolution 800*480
    display_mode="lcd"
    rgb888p_size=[640,360]
    confidence_threshold = 0.5
    nms_threshold=0.45
    # Initialize PipeLine
    pl=PipeLine(rgb888p_size=rgb888p_size,display_mode=display_mode)
    pl.create()
    display_size=pl.get_display_size()
    # Initialize the YOLOv8 instance
    yolo=YOLOv8(task_type="detect",mode="video",kmodel_path=kmodel_path,labels=labels,rgb888p_size=rgb888p_size,model_input_size=model_input_size,display_size=display_size,conf_thresh=confidence_threshold,nms_thresh=nms_threshold,max_boxes_num=50,debug_mode=0)
    yolo.config_preprocess()
    while True:
        with ScopedTiming("total",1):
            # Inference frame by frame
            img=pl.get_frame()
            res=yolo.run(img)
            yolo.draw_result(res,pl.osd_img)
            pl.show_image()
            gc.collect()
    yolo.deinit()
    pl.destroy()

The above code gives an example of using YOLOv8 for video inference.

YOLO11 Class

Constructor

Description

The encapsulated YOLO11 module constructor, initializes the YOLO11 type to obtain a YOLO11 instance.

Syntax

from libs.YOLO import YOLO11

yolo=YOLO11(task_type="segment",mode="image",kmodel_path="yolo11_det.kmodel",labels=["apple","banana","orange"],rgb888p_size=[1280,720],model_input_size=[320,320],conf_thresh=0.5,nms_thresh=0.25,mask_thresh=0.5,max_boxes_num=50,debug_mode=0)

Parameters

Parameter Name	Description	Description	Type
task_type	Task Type	Supports four types of tasks, optional values are ‘classify’/’detect’/’segment’/’obb’;	str
mode	Inference Mode	Supports two inference modes, optional values are ‘image’/’video’, ‘image’ means inference on images, ‘video’ means inference on real-time video streams captured by the camera;	str
kmodel_path	kmodel Path	Path of the kmodel copied to the development board;	str
labels	Class Label List	Label names for different categories;	list[str]
rgb888p_size	Inference Frame Resolution	Resolution of the current inference frame, such as [1920,1080], [1280,720], [640,640];	list[int]
model_input_size	Model Input Resolution	Input resolution when training the YOLO11 model, such as [224,224], [320,320], [640,640];	list[int]
display_size	Display Resolution	Set when the inference mode is ‘video’, supports hdmi([1920,1080]) and lcd([800,480]);	list[int]
conf_thresh	Confidence Threshold	Class confidence threshold for classification tasks, target confidence threshold for detection and segmentation tasks, such as 0.5;	float [0~1]
nms_thresh	NMS Threshold	Non-maximum suppression threshold, required for detection and segmentation tasks;	float [0~1]
mask_thresh	Mask Threshold	Binarization threshold for segmenting objects within detection boxes in segmentation tasks;	float [0~1]
max_boxes_num	Maximum Detection Boxes	Maximum number of detection boxes allowed to be returned in one frame of image;	int
kp_num	Number of Keypoints	Number of keypoints in the keypoint detection task;	int
kp_dim	Keypoint Dimension	Dimension of keypoints in the keypoint detection task, only supports 2 and 3, determined by the trained model;	int【2/3】
debug_mode	Debug Mode	Whether the timing function is enabled, optional 0/1, 0 means no timing, 1 means timing;	int [0/1]

Return Value

Return Value	Description
YOLO11	YOLO11 instance

config_preprocess

Description

YOLO11 preprocessing configuration function.

Syntax

yolo.config_preprocess()

Parameters

Parameter Name	Description	Input / Output	Description
None

Return Value

Return Value	Description
None

run

Description

Infer one frame of image, return the inference result for use by the draw_result method. Classification tasks return class indices and scores, detection tasks return lists of detection box positions, scores, and class indices, segmentation tasks return mask results and lists of detection box positions, scores, and class indices, oriented object detection returns lists of four-point coordinates of oriented detection boxes, keypoint detection returns lists of detection box positions, class indices, scores, and keypoints.

Syntax

res=yolo.run(img)

Parameters

Parameter Name	Description	Input / Output	Description
img	The image to be inferred in the format of ulab.numpy.ndarray, or a frame of image obtained from the video stream via get_frame	Input

Return Value

Return Value

Description

res

Model post-processing result, different tasks return different values, classification tasks return class indices and scores, detection tasks return lists of detection box positions, scores, and class indices, segmentation tasks return mask results and lists of detection box positions, scores, and class indices, oriented object detection returns lists of four-point coordinates of oriented detection boxes, keypoint detection returns lists of detection box positions, class indices, scores, and keypoints.

draw_result

Description

Draw the YOLO11 inference result on the screen or image.

Syntax

yolo.draw_result(res,img_ori)

Parameters

Parameter Name	Description	Input / Output	Description
res	Inference result of YOLO11	Input
img_ori	Image instance to be drawn	Input	From read_img or pl.osd_img

Return Value

Return Value	Description
None

Example Program

The following gives an example program for the YOLO11 segmentation task:

from libs.YOLO import YOLO11
from libs.Utils import *
import os,sys,gc
import ulab.numpy as np
import image

if __name__=="__main__":
    # This is only an example, please modify it to your own test image, model path, label name, and model input size for custom scenarios
    img_path="/sdcard/examples/utils/test_fruit.jpg"
    kmodel_path="/sdcard/examples/kmodel/fruit_det_yolo11n_320.kmodel"
    labels = ["apple","banana","orange"]
    model_input_size=[320,320]

    confidence_threshold = 0.5
    nms_threshold=0.45
    img,img_ori=read_image(img_path)
    rgb888p_size=[img.shape[2],img.shape[1]]
    # Initialize YOLO11 instance
    yolo=YOLO11(task_type="detect",mode="image",kmodel_path=kmodel_path,labels=labels,rgb888p_size=rgb888p_size,model_input_size=model_input_size,conf_thresh=confidence_threshold,nms_thresh=nms_threshold,max_boxes_num=50,debug_mode=0)
    yolo.config_preprocess()
    res=yolo.run(img)
    yolo.draw_result(res,img_ori)
    yolo.deinit()
    gc.collect()

The above code provides the code for using YOLO11 for image inference.

from libs.PipeLine import PipeLine
from libs.YOLO import YOLO11
from libs.Utils import *
import os,sys,gc
import ulab.numpy as np
import image

if __name__=="__main__":
    # This is only an example, please modify it to your own model path, label name, and model input size for custom scenarios
    kmodel_path="/sdcard/examples/kmodel/fruit_det_yolo11n_320.kmodel"
    labels = ["apple","banana","orange"]
    model_input_size=[320,320]

    # Add display mode, default is hdmi, optional hdmi/lcd/lt9611/st7701/hx8399, where hdmi is set to lt9611 by default with resolution 1920*1080; lcd is set to st7701 by default with resolution 800*480
    display_mode="lcd"
    rgb888p_size=[640,360]
    confidence_threshold = 0.5
    nms_threshold=0.45
    # Initialize PipeLine
    pl=PipeLine(rgb888p_size=rgb888p_size,display_mode=display_mode)
    pl.create()
    display_size=pl.get_display_size()
    # Initialize YOLO11 instance
    yolo=YOLO11(task_type="detect",mode="video",kmodel_path=kmodel_path,labels=labels,rgb888p_size=rgb888p_size,model_input_size=model_input_size,display_size=display_size,conf_thresh=confidence_threshold,nms_thresh=nms_threshold,max_boxes_num=50,debug_mode=0)
    yolo.config_preprocess()
    while True:
        with ScopedTiming("total",1):
            # Inference frame by frame
            img=pl.get_frame()
            res=yolo.run(img)
            yolo.draw_result(res,pl.osd_img)
            pl.show_image()
            gc.collect()
    yolo.deinit()
    pl.destroy()

The above code provides the code for using YOLO11 for video inference.

YOLO26 Class

Constructor

Description

Encapsulated YOLO26 module constructor, initializes the YOLO26 type to obtain a YOLO26 instance.

Syntax

from libs.YOLO import YOLO26

yolo=YOLO26(task_type="pose",mode="image",kmodel_path="yolo26n_pose.kmodel",labels=["people"],rgb888p_size=[1280,720],model_input_size=[320,320],conf_thresh=0.5,mask_thresh=0.5,kp_num=17,kp_dim=3,max_boxes_num=50,debug_mode=0)

Parameters

Parameter Name	Description	Notes	Type
task_type	Task type	Supports four types of tasks, options are ‘classify’/’detect’/’segment’/’obb’;	str
mode	Inference mode	Supports two inference modes, options are ‘image’/’video’; ‘image’ means inference on an image, ‘video’ means inference on real-time video stream captured by camera;	str
kmodel_path	kmodel path	The kmodel path copied to the development board;	str
labels	Class label list	Label names for different classes;	list[str]
rgb888p_size	Inference frame resolution	Resolution of the current inference frame, e.g., [1920,1080], [1280,720], [640,640];	list[int]
model_input_size	Model input resolution	Input resolution when training the YOLO11 model, e.g., [224,224], [320,320], [640,640];	list[int]
display_size	Display resolution	Set when the inference mode is ‘video’, supports hdmi([1920,1080]) and lcd([800,480]);	list[int]
conf_thresh	Confidence threshold	Class confidence threshold for classification task, object confidence threshold for detection and segmentation tasks, e.g., 0.5;	float [0~1]
mask_thresh	Mask threshold	Binarization threshold for segmenting objects within detection boxes in segmentation tasks;	float [0~1]
max_boxes_num	Maximum number of detection boxes	Maximum number of detection boxes allowed to be returned in one frame of image;	int
kp_num	Number of keypoints	Number of keypoints in the keypoint detection task;	int
kp_dim	Keypoint dimension	Dimension of keypoints in the keypoint detection task, only 2 and 3 are supported, determined by the trained model;	int[2/3]
debug_mode	Debug mode	Whether the timing function takes effect, options 0/1, 0 for no timing, 1 for timing;	int [0/1]

Note: The YOLO26 module no longer includes the nms_thresh parameter, because the YOLO26 model directly outputs results with semantic information.

Return Value

Return Value	Description
YOLO26	YOLO26 instance

config_preprocess

Description

YOLO26 preprocessing configuration function.

Syntax

yolo.config_preprocess()

Parameters

Parameter Name	Description	Input / Output	Notes
None

Return Value

Return Value	Description
None

run

Description

Performs inference on one frame of image, returns the inference result for use by the draw_result method. The classification task returns the class index and score, the detection task returns a list of detection box positions, scores, and class indices, the segmentation task returns the mask result and a list of detection box positions, scores, and class indices, the oriented object detection returns a list of four-point coordinates of the oriented detection boxes, and the keypoint detection returns the detection box positions, class indices, scores, and keypoints list.

Syntax

res=yolo.run(img)

Parameters

Parameter Name	Description	Input / Output	Notes
img	The image to be inferred in the format of ulab.numpy.ndarray, or a frame of image obtained from the video stream via get_frame	Input

Return Value

Return Value

Description

res

Model post-processing result, the return value varies for different tasks. The classification task returns the class index and score, the detection task returns a list of detection box positions, scores, and class indices, the segmentation task returns the mask result and a list of detection box positions, scores, and class indices, the oriented object detection returns a list of four-point coordinates of the oriented detection boxes, and the keypoint detection returns the detection box positions, class indices, scores, and keypoints list.

draw_result

Description

Draws the YOLO26 inference result on the screen or image.

Syntax

yolo.draw_result(res,img_ori)

Parameters

Parameter Name	Description	Input / Output	Notes
res	Inference result of YOLO26	Input
img_ori	Image instance to be drawn	Input	From read_img or pl.osd_img

Return Value

Return Value	Description
None

Example Program

Below is an example program for the YOLO26 segmentation task:

from libs.YOLO import YOLO26
from libs.Utils import *
import os,sys,gc
import ulab.numpy as np
import image

if __name__=="__main__":
    # This is only an example, please modify to your own test image, model path, label names, model input size, number of keypoints, and keypoint dimension for custom scenarios
    img_path="/sdcard/examples/utils/test_pose.jpg"
    kmodel_path="/sdcard/examples/kmodel/yolo26n-pose.kmodel"
    labels = ['person']
    model_input_size=[320,320]
    kp_num=17
    kp_dim=3

    confidence_threshold = 0.5
    nms_threshold=0.45
    img,img_ori=read_image(img_path)
    rgb888p_size=[img.shape[2],img.shape[1]]
    # Initialize YOLO26 model
    yolo=YOLO26(task_type="pose",mode="image",kmodel_path=kmodel_path,labels=labels,rgb888p_size=rgb888p_size,model_input_size=model_input_size,kp_num=kp_num,kp_dim=kp_dim,conf_thresh=confidence_threshold,max_boxes_num=100,debug_mode=0)
    yolo.config_preprocess()
    res=yolo.run(img)
    print(res)
    yolo.draw_result(res,img_ori)
    yolo.deinit()
    gc.collect()

The above code gives the code for performing image inference using YOLO26.

from libs.PipeLine import PipeLine
from libs.YOLO import YOLO26
from libs.Utils import *
import os,sys,gc
import ulab.numpy as np
import image

if __name__=="__main__":
    # This is only an example, please modify to your own model path, label names, model input size, number of keypoints, and keypoint dimension for custom scenarios
    kmodel_path="/sdcard/examples/kmodel/yolo26n-pose.kmodel"
    labels = ["person"]
    model_input_size=[320,320]
    kp_num=17
    kp_dim=3

    # Add display mode, default is hdmi, options are hdmi/lcd/lt9611/st7701/hx8399, among which hdmi defaults to lt9611 with resolution 1920*1080; lcd defaults to st7701 with resolution 800*480
    display_mode="lcd"
    rgb888p_size=[320,320]
    confidence_threshold = 0.5
    pl=PipeLine(rgb888p_size=rgb888p_size,display_mode=display_mode)
    pl.create()
    display_size=pl.get_display_size()
    # Initialize YOLO26 model
    yolo=YOLO26(task_type="pose",mode="video",kmodel_path=kmodel_path,labels=labels,rgb888p_size=rgb888p_size,model_input_size=model_input_size,display_size=display_size,kp_num=kp_num,kp_dim=kp_dim,conf_thresh=confidence_threshold,max_boxes_num=50,debug_mode=0)
    yolo.config_preprocess()
    while True:
        with ScopedTiming("total",1):
            img=pl.get_frame()
            res=yolo.run(img)
            yolo.draw_result(res,pl.osd_img)
            pl.show_image()
            gc.collect()
    yolo.deinit()
    pl.destroy()

The above code gives the code for performing video inference using YOLO26.