Comments (3)
Hello! Thanks for your questions. Let me provide some clarity:
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YOLO detection models are designed to perform both localization (finding bounding boxes for objects) and classification (labeling those objects). If you need only localization, you might need to modify the model output layers or use another method designed for localization only.
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The difference between a classification model and classification in a detection model is mainly about their objectives. A classification model categorizes a whole image into classes. In contrast, classification in a detection model involves identifying classes of multiple objects within a single image, each with a corresponding bounding box.
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Yes, the YOLOv8 classification model can perform multi-label classification, where multiple labels can be assigned to a single image, indicating the presence of various objects or attributes.
Hope this answers your queries! Let me know if you need further assistance. 😊
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Thank you for your answer!
Q1. Then in classificaton model, model is using grid search like classfication in detection model? if not or whole image classification?
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Hello!
In a classification model, the entire image is processed as a single input to classify it into categories. It does not use a grid search like in detection models. Detection models, such as YOLO, divide the image into grids to locate and classify multiple objects within the image. In contrast, classification models focus on identifying the main subject or the overall scene of the image without locating objects.
If you have more questions or need further clarification, feel free to ask! 😊
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