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      Train a CenterNet model using custom keypoints 29
    

    
      
        opened 
        
          
        
        Mar 30, 2021
      
      


      
        
          
          bileki
        
      
    

    
        models:research
        type:docs
    
  


  
    # Prerequisites

Please answer the following question for yourself before subm…itting an issue.

- [*] I checked to make sure that this issue has not been filed already.

## 1. The entire URL of the documentation with the issue

https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/configuring_jobs.md

## 2. Describe the issue

I am trying to train a CenterNet model using custom keypoints.
However, I can't find any documentation or example on how to configure the CenterNet pipeline file.

In https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md, the examples are just about a human pose and there is not even an explanation on how to set the **keypoint_estimation_task**, or what the **value** parameter of **keypoint_label_to_std** means or how to calculate it. The same for **keypoint_label_to_sigmas**.

Example of **centernet_mobilenetv2fpn_512x512_coco17_kpts.tar.gz**:

>     keypoint_label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
>     keypoint_estimation_task {
>       task_name: "human_pose"
>       task_loss_weight: 1.0
>       loss {
>         localization_loss {
>           l1_localization_loss {
>           }
>         }
>         classification_loss {
>           penalty_reduced_logistic_focal_loss {
>             alpha: 2.0
>             beta: 4.0
>           }
>         }
>       }
>       keypoint_class_name: "/m/01g317"
>       keypoint_label_to_std {
>         key: "left_ankle"
>         value: 0.89
>       }
>       keypoint_label_to_std {
>         key: "left_ear"
>         value: 0.35
>       }
>       keypoint_label_to_std {
>         key: "left_elbow"
>         value: 0.72
>       }
>       ....
>       keypoint_regression_loss_weight: 0.1
>       keypoint_heatmap_loss_weight: 1.0
>       keypoint_offset_loss_weight: 1.0
>       offset_peak_radius: 3
>       per_keypoint_offset: true
>     }