input: raw image output: bboxes, classification info. (if there no object in image, not return empty, return a specified value)
input image-->detection--> post_detection: filter object and crop object and preprocess for classification --> classification --> post_classification
In this template i want detect person and distinguish whether they are wearing vest.
The detection model is yolov5.pytorch, export to model.onnx, input-size:640, detect two class: person and head, in this demo, only use person my yolov5
The post detection model, write by python backend, first, get detection bboxes, then, according to origin input image and detection bboxes to crop person, and format every crop image to classification input.
notice: In order to prevent the subsequent classification error caused by no person in detection result, a random image is appended at the end of the crop image list.
The classification model is efficient-net.keras, input-size: 260, return 3 class, background, wear vest, no wear vest.
The post classification model. In this demo, only use one classification mode, so not implementation this.
