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examples/YOLOv8-Classification-ONNXRuntime-Python/README.md

@@ -40,4 +40,4 @@ After installing the required packages, you can run YOLOv8 classification with t
 python yolov8_classifier.py --model yolov8n.onnx --img image.jpg
 ```
 
-Replace yolov8n.onnx with the path to your YOLOv8 ONNX model file and image.jpg with the path to your input image.
+Replace yolov8n.onnx with the path to your YOLOv8 ONNX model file and image.jpg with the path to your input image.

examples/YOLOv8-Classification-ONNXRuntime-Python/yolov8_classifier.py

@@ -1,10 +1,11 @@
 import argparse
+
 import cv2
 import numpy as np
 import onnxruntime as ort
 import torch
 
-from ultralytics.utils import ASSETS, yaml_load
+from ultralytics.utils import yaml_load
 from ultralytics.utils.checks import check_requirements, check_yaml
 
 
@@ -35,7 +36,6 @@ class YOLOv8Classifier:
         Args:
             class_index (int): Index of the detected class to annotate.
         """
-
         # Draw the class label text on the image
         label = f"{self.class_names[class_index]}"
         cv2.putText(self.image, label, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
@@ -109,6 +109,7 @@ class YOLOv8Classifier:
         # Process and return the model output
         return self.process_output(model_output)
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--model", type=str, default="yolo8n-cls.onnx", help="Path to the ONNX model file.")