Retraining peoplenet model with own images

[Morganh]

Share one experiment with you.
For your above test image, I just run the ngc resnet34 unpruned tlt model directly.
And it can get expected result. So the pretrained model is fine.

Step:

1. Trigger tlt 2.0_py3 docker
2. Download tlt pretrained model
   $ wget https://api.ngc.nvidia.com/v2/models/nvidia/tlt_peoplenet/versions/unpruned_v2.0/files/resnet34_peoplenet.tlt
3. Download your test image
   $ wget https://global.discourse-cdn.com/nvidia/original/3X/9/9/9937f27d49d4adf2432969222cb12fc004b5f75e.jpeg
4. Create inference spec
   $ vim detectnet_v2_inference_kitti_tlt.txt

inferencer_config{

target_classes: “Person”
target_classes: “Bag”
target_classes: “Face”

image_width: 960
image_height: 544

image_channels: 3
batch_size: 16
gpu_index: 0

tlt_config{
model: “/workspace/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”
confidence_threshold: 0.9
bbox_color{
R: 0
G: 255
B: 0
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: 0.3
dbscan_min_samples: 0.05
minimum_bounding_box_height: 4
}
}
}
classwise_bbox_handler_config{
key:“Bag”
value: {
confidence_model: “aggregate_cov”
output_map: “Bag”
confidence_threshold: 0.9
bbox_color{
R: 0
G: 255
B: 255
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: 0.3
dbscan_min_samples: 0.05
minimum_bounding_box_height: 4
}
}
}
classwise_bbox_handler_config{
key:“Face”
value: {
confidence_model: “aggregate_cov”
output_map: “Face”
confidence_threshold: 0.9
bbox_color{
R: 255
G: 0
B: 0
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: 0.3
dbscan_min_samples: 0.05
minimum_bounding_box_height: 4
}
}
}
classwise_bbox_handler_config{
key:“default”
value: {
confidence_model: “aggregate_cov”
confidence_threshold: 0.9
bbox_color{
R: 255
G: 0
B: 0
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: 0.3
dbscan_min_samples: 0.05
minimum_bounding_box_height: 4
}
}
}
}

3. Run inference
   $ tlt-infer detectnet_v2 -e detectnet_v2_inference_kitti_tlt.txt -o output -i 9937f27d49d4adf2432969222cb12fc004b5f75e.jpeg -k tlt_encode

4. Get below result