• Network Type Detectnet_v2
Hi, I did not find good documentation about tao inference
. I know that it generates some .txt in kitti format (15 values) and 1 extra value which its supposed to be the confidence right? but there are values from 0.* to 131. 8 for example when I use “aggregate_cov” and I dont understand that part.
It supposed to be values between 0-1 or between 0-100?
In the spec file of inference there is “aggregate_cov” and “mean_cov” for confidence _model
. When I tried “mean_cov” there was a warning like:
/usr/local/lib/python3.6/dist-packages/iva/detectnet_v2/postprocessor/utilities.py:352: RuntimeWarning: invalid value encountered in double_scalars
And it generates only empties .txt
So my last question, how can I get the confidence for each bbox?
leo2105:
aggregate_conv
It is aggregate_cov instead of aggregate_conv.
And the description is mentioned in DetectNet_v2 - NVIDIA Docs
Algorithm to compute the final confidence of the clustered bboxes. In the aggregate_cov mode, the final confidence of a detection is the sum of the confidences of all the candidate bboxes in a cluster. In mean_cov mode, the final confidence is the mean confidence of all the bboxes in the cluster.
Thanks for replying.
Yeah, before writting this post I had read the documentation. I dont want the sum which is from 0.* to 131.* (in my case)
I will rephrase my question. Is there any way to get a confidence between 0-1 or 0-100 for each bbox?
I was thinking about normalizing the aggregate_cov maybe, i dont know if it is correct.
Please use mean_cov mode. It ranges from 0.0 to 1.0.
leo2105:
In the spec file of inference there is “aggregate_cov” and “mean_cov” for confidence _model
. When I tried “mean_cov” there was a warning like:
/usr/local/lib/python3.6/dist-packages/iva/detectnet_v2/postprocessor/utilities.py:352: RuntimeWarning: invalid value encountered in double_scalars
And it generates only empties .txt
Yeah, but as I said at the beginning it returns this empty txts
It is not expected. Did you ever try running with official jupyter notebook? TAO Toolkit Quick Start Guide — TAO Toolkit 3.22.05 documentation
There is an example inference spec file.
Yep, the same thing, using aggregate_cov
it works perfect, but for mean_cov
occurs that error
my inference spec file:
inferencer_config{
# defining target class names for the experiment.
# Note: This must be mentioned in order of the networks classes.
target_classes: "car"
target_classes: "two-wheeler"
target_classes: "person"
target_classes: "stop_sign"
# Inference dimensions.
image_width: 960
image_height: 544
# Must match what the model was trained for.
image_channels: 3
batch_size: 24
gpu_index: 0
# model handler config
tlt_config{
model: "/trafficcamnet_activeLearning/experiments_AL/model_nvactivelearning_1_100.tlt"
}
}
bbox_handler_config{
kitti_dump: true
disable_overlay: false
overlay_linewidth: 2
classwise_bbox_handler_config{
key:"car"
value: {
confidence_model: "aggregate_cov"
output_map: "car"
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:"two-wheeler"
value: {
confidence_model: "aggregate_cov"
output_map: "two-wheeler"
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:"person"
value: {
confidence_model: "aggregate_cov"
output_map: "person"
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:"stop_sign"
value: {
confidence_model: "aggregate_cov"
output_map: "stop_sign"
bbox_color{
R: 0
G: 0
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:"default"
value: {
confidence_model: "aggregate_cov"
bbox_color{
R: 255
G: 255
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
}
}
}
}
It looks like a problem of division by zero that you didnt solve…
Can you share the spec when you set to “mean_cov” mode?
Yep, I just changed that word.
inferencer_config{
# defining target class names for the experiment.
# Note: This must be mentioned in order of the networks classes.
target_classes: "car"
target_classes: "two-wheeler"
target_classes: "person"
target_classes: "stop_sign"
# Inference dimensions.
image_width: 960
image_height: 544
# Must match what the model was trained for.
image_channels: 3
batch_size: 24
gpu_index: 0
# model handler config
tlt_config{
model: "/trafficcamnet_activeLearning/experiments_AL/model_nvactivelearning_1_100.tlt"
}
}
bbox_handler_config{
kitti_dump: true
disable_overlay: false
overlay_linewidth: 2
classwise_bbox_handler_config{
key:"car"
value: {
confidence_model: "mean_cov"
output_map: "car"
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:"two-wheeler"
value: {
confidence_model: "mean_cov"
output_map: "two-wheeler"
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:"person"
value: {
confidence_model: "mean_cov"
output_map: "person"
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:"stop_sign"
value: {
confidence_model: "mean_cov"
output_map: "stop_sign"
bbox_color{
R: 0
G: 0
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:"default"
value: {
confidence_model: "mean_cov"
bbox_color{
R: 255
G: 255
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
}
}
}
}
Please set a lower dbscan_confidence_threshold for all classes and retry.
Ok… it generates the bboxes in kitti format and confidence ranged 0 - 1.
but the warning is still there, I dont know if in the inference it will loss some bboxes because of the warning.
Thanks
system
Closed
March 29, 2022, 2:27am
16
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