PeopleNet not detecting bags

Accelerated Computing Intelligent Video Analytics TAO Toolkit
6

I tried to evaluate the trainable model directly using the Jupyter notebook for detectnet_v2. I used the command:

!tao detectnet_v2 inference -e $SPECS_DIR/my_inference.txt \
                            -o $USER_EXPERIMENT_DIR/my_data_testing \
                            -i $USER_EXPERIMENT_DIR/my_data \
                            -k $KEY

The folder my_data contains my testing photos. The variables are set in the section 0 in the Jupyter notebook. I ran whole this Jupyter notebook before and all seems to work there. The file my_inference.txt contains:

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: 1
  gpu_index: 0
  # model handler config
  tlt_config{
    model: "/workspace/tao-experiments/detectnet_v2/resnet34_peoplenet.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
      }
    }
  }
}

When I use this, all pictures are evaluated. Persons and faces are detected but no bags.

bagz2
bagz2960×544 59.9 KB

There are three thresholds. coverage_threshold, dbscan_min_samples and dbscan_confidence_threshold. If I set them all to 0, there are many detections. However, if I set any of those thresholds to 0.00000001, there are no bag detections at all. Persons and faces are detected normally.

I test it on 8 photos with 11 bags in total. None of them is detected, not even a single false-positive.

EDIT: I set the resolution to 940×544, still the same result