how to Improve YOLOv8 Performance on Similar looking Signboard Classes #13066
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To enhance YOLOv8 performance on similar-looking signboard classes, consider the following strategies:
Here's a simple example of how you might adjust the training process: from ultralytics import YOLO
# Load your model
model = YOLO('yolov8n.pt')
# Train with custom augmentations and hyperparameters
model.train(data='your_dataset.yaml', imgsz=640, epochs=100, augment=True, hyp='your_hyperparams.yaml') These steps should help mitigate issues arising from the visual similarities between the classes and improve the model's accuracy. |
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Improving YOLOv8 Performance on Similar Signboard Classes
Project Overview:
I am currently working on a computer vision project using YOLOv8 for custom object detection and classification. My goal is to classify different types of signboards. I have categorized the signboards into four classes:
1)Stop Boards
2)General Sign-Boards (includes various types of road signs)
3)Trail Signage Boards (informational signs along trails)
4)Flooding Indication Signboards
Example Images:
### Images of class 2 :-
images of class 4 : -
Question:
I am concerned about the potential performance issues due to the similarity between the "General Sign-Boards" (Class 2) and the "Flooding Indication Signboards" (Class 4). Both classes contain sign boards that are visually similar in terms of shape, color, and sometimes text or symbols.
Specific Concern:
Will the model perform poorly due to the visual similarity between Class 2 and Class 4? How can I ensure that the model focuses on unique features such as specific text patterns, symbols, or contextual elements rather than just shapes and colors?
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