YOLOv8_ONNX
Use the YOLOv8 model in ONNX to perform the following tasks:
- object detection
- instance segmentation
- pose estimation
The above results are from YOLOv8n.
Examples
- Convert model to ONNX
python export.py- Inference
import cv2
from YOLOv8_ONNX import YOLOv8, YOLOv8Seg, YOLOv8Pose
yolo = YOLOv8("models/yolov8n.onnx", conf_thres=0.3, iou_thres=0.5)
# yolo = YOLOv8Seg("models/yolov8n-seg.onnx", conf_thres=0.3, iou_thres=0.5)
# yolo = YOLOv8Pose("models/yolov8n-pose.onnx", conf_thres=0.3, iou_thres=0.5)
img = cv2.imread("assets/bus.jpg", cv2.IMREAD_COLOR)
# Detect Objects
yolo(img)
# Draw detections
combined_img = yolo.draw_results(img)
cv2.namedWindow("Output", cv2.WINDOW_NORMAL)
cv2.imshow("Output", combined_img)
cv2.waitKey(0)- Quantization
python quant.py- Benchmark
python perf.py