Inference was interrupted occurred in the process of doing peoplenet_transformer inference

Hello. When I do the inference of PeopleNet Transformer, the process was interrupted without any error messages.

What made this interruption happend ? How should I do to deal with the problem ?

  1. My data is jpg and jpeg files

  2. The model I used was resnet50_peoplenet_transformer.tlt from NGC

Here is my command

sudo docker run -it --rm -v /home/ubuntu/tao_test_2023/peoplenet_transformer:/workspace/tao-experiments/peoplenet_transformer/ 
                           nvcr.io/nvidia/tao/tao-toolkit:4.0.1-tf1.15.5 
                           detectnet_v2 inference 
                         -e /workspace/tao-experiments/peoplenet_transformer/specs.txt 
                         -o /workspace/tao-experiments/peoplenet_transformer 
                         -i /workspace/tao-experiments/peoplenet_transformer/data 
                         -k tlt_encode

Here is my spec file

inferencer_config{  # defining target class names for the experiment.
  # Note: This must be mentioned in order of the networks classes.
  target_classes: "person"
  target_classes: "bag"
  target_classes: "face"
  # Inference dimensions.
  image_width: 960
  image_height: 544
  # Must match what the model was trained for.
  image_channels: 3
  batch_size: 16
  gpu_index: 0
  # model handler config
  tlt_config{
    model: "/workspace/tao-experiments/peoplenet_transformer/resnet50_peoplenet_transformer.tlt"
  }
}
bbox_handler_config{
  kitti_dump: true
  disable_overlay: false
  overlay_linewidth: 2
  classwise_bbox_handler_config{
    key:"person"
    value: {
      confidence_model: "aggregate_cov"
      output_map: "person"
      bbox_color{
        R: 0
        G: 255
        B: 0
      }
      clustering_config{
        clustering_algorithm: DBSCAN
        coverage_threshold: 0.005
        dbscan_eps: 0.3
        dbscan_min_samples: 0.05
        dbscan_confidence_threshold: 0.9
        minimum_bounding_box_height: 4
      }
    }
  }
  classwise_bbox_handler_config{
    key:"bag"
    value: {
      confidence_model: "aggregate_cov"
      output_map: "bag"
      bbox_color{
        R: 0
        G: 255
        B: 255
      }
      clustering_config{
        clustering_algorithm: DBSCAN
        coverage_threshold: 0.005
        dbscan_eps: 0.3
        dbscan_min_samples: 0.05
        dbscan_confidence_threshold: 0.9
        minimum_bounding_box_height: 4
      }
    }
  }
  classwise_bbox_handler_config{
    key:"face"
    value: {
      confidence_model: "aggregate_cov"
      output_map: "face"
      bbox_color{
        R: 255
        G: 0
        B: 0
      }
      clustering_config{
        clustering_algorithm: DBSCAN
        coverage_threshold: 0.005
        dbscan_eps: 0.3
        dbscan_min_samples: 0.05
        dbscan_confidence_threshold: 0.9
        minimum_bounding_box_height: 4
      }
    }
  }
  classwise_bbox_handler_config{
    key:"default"
    value: {
      confidence_model: "aggregate_cov"
      bbox_color{
        R: 255
        G: 0
        B: 0
      }
      clustering_config{
        clustering_algorithm: DBSCAN
        dbscan_confidence_threshold: 0.9
        coverage_threshold: 0.005
        dbscan_eps: 0.3
        dbscan_min_samples: 0.05
        minimum_bounding_box_height: 4
      }
    }
  }
}

There is no update from you for a period, assuming this is not an issue anymore. Hence we are closing this topic. If need further support, please open a new one. Thanks

This model was trained using the Deformable DETR network instead of detectnet_v2 network.

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