Two Stage Classification using YOLOv5

Shahji55 commented 3 years ago

I have a dataset containing bounding box of face annotation with the following 5 class age labels:

3-12
13-19
20-39
40-59
60+

I was wondering if it's possible to use a two stage approach where I first use Yolov5 to detect the face crop and then use that as input for a model (EfficientNet-B0, Resnet) to perform classification with features transferred between the detector and the classifier?

Note: I am assuming yolo offers something like that with classify argument in detect.py. But I am not sure how to train in end to end settings and how to prepare data for yolo model.

github-actions[bot] commented 3 years ago

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glenn-jocher commented 3 years ago

@Shahji55 yes some organizations have created two stage (detect-classify) solutions. In general they may be useful for reducing FPs. See TowerScout for a deployed example of YOLOv5 paired with a second stage EfficientNet model. https://groups.ischool.berkeley.edu/TowerScout/

See also 'Second-stage classifier in detect.py' https://github.com/ultralytics/yolov5/issues/2260

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