# Object detection grad_cam heatmap## 1.IntroductionCalculate the cam (class activation map) of the object predict bbox based on the backbone feature map## 2.Usage* Taking PP-YOLOE as an example, after preparing the data, specify the network configuration file, model weight address, image path and output folder path, and then use the script to call tools/cam_ppdet.py to calculate the grad_cam heat map of the prediction box. Below is an example run script.```shellpython tools/cam_ppdet.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --infer_img demo/000000014439.jpg --cam_out cam_ppyoloe -oweights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams```***Arguments**| FLAG | description || :----------------------: |:---------------------------------------------------------------------------------------------------------------------------------:|| -c | Select config file || --infer_img | Image path || --cam_out | Directory for output || -o | Set parameters in configure file, for example: -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams |* result<center><imgsrc="../images/grad_cam_ppyoloe_demo.jpg"width="500"></center><br><center>cam_ppyoloe/225.jpg</center></br>## 3.Currently supports networks based on FasterRCNN and YOLOv3 series.* FasterRCNN bbox heat map visualization script```bashpython tools/cam_ppdet.py -c configs/faster_rcnn/faster_rcnn_r50_vd_fpn_2x_coco.yml --infer_img demo/000000014439.jpg --cam_out cam_faster_rcnn -oweights=https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams```* PPYOLOE bbox heat map visualization script```bashpython tools/cam_ppdet.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --infer_img demo/000000014439.jpg --cam_out cam_ppyoloe -oweights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams```