• Hardware (NVIDIA RTX 3080Ti)
• Network Type (Detectnet_v2)
• TLT Version (TAO 5.5.0)
• Training spec file(random_seed: 42
dataset_config {
data_sources {
tfrecords_path: “/workspace/tao-experiments/detectnet_v2/dataset/tfrecords/tfrecords*”
image_directory_path: “/workspace/tao-experiments/detectnet_v2/dataset”
}
image_extension: “png”
target_class_mapping {
key: “car”
value: “car”
}
target_class_mapping {
key: “cyclist”
value: “cyclist”
}
target_class_mapping {
key: “pedestrian”
value: “pedestrian”
}
target_class_mapping {
key: “person_sitting”
value: “pedestrian”
}
target_class_mapping {
key: “truck”
value: “car”
}
target_class_mapping {
key: “van”
value: “car”
}
validation_data_source {
tfrecords_path: “/workspace/tao-experiments/detectnet_v2/dataset/tfrecords/tfrecords*”
image_directory_path: “/workspace/tao-experiments/detectnet_v2/dataset”
}
}
augmentation_config {
preprocessing {
output_image_width: 1248
output_image_height: 384
min_bbox_width: 1.0
min_bbox_height: 1.0
output_image_channel: 3
}
spatial_augmentation {
hflip_probability: 0.5
zoom_min: 1.0
zoom_max: 1.0
translate_max_x: 8.0
translate_max_y: 8.0
}
color_augmentation {
hue_rotation_max: 25.0
saturation_shift_max: 0.20000000298023224
contrast_scale_max: 0.10000000149011612
contrast_center: 0.5
}
}
postprocessing_config {
target_class_config {
key: “car”
value {
clustering_config {
coverage_threshold: 0.004999999888241291
minimum_bounding_box_height: 20
dbscan_eps: 0.15000000596046448
dbscan_min_samples: 1
}
}
}
target_class_config {
key: “cyclist”
value {
clustering_config {
coverage_threshold: 0.004999999888241291
minimum_bounding_box_height: 20
dbscan_eps: 0.15000000596046448
dbscan_min_samples: 1
}
}
}
target_class_config {
key: “pedestrian”
value {
clustering_config {
coverage_threshold: 0.004999999888241291
minimum_bounding_box_height: 20
dbscan_eps: 0.15000000596046448
dbscan_min_samples: 1
}
}
}
}
model_config {
pretrained_model_file: “/workspace/tao-experiments/detectnet_v2/pretrained_detectnet_v2_vresnet18/resnet18.hdf5”
num_layers: 18
use_batch_norm: true
load_graph: False
objective_set {
bbox {
scale: 35.0
offset: 0.5
}
cov {
}
}
freeze_blocks: 0.0
freeze_blocks: 1.0
arch: “resnet”
all_projections: true
}
evaluation_config {
validation_period_during_training: 10
first_validation_epoch: 1
minimum_detection_ground_truth_overlap {
key: “car”
value: 0.699999988079071
}
minimum_detection_ground_truth_overlap {
key: “cyclist”
value: 0.5
}
minimum_detection_ground_truth_overlap {
key: “pedestrian”
value: 0.5
}
evaluation_box_config {
key: “car”
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
evaluation_box_config {
key: “cyclist”
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
evaluation_box_config {
key: “pedestrian”
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
average_precision_mode: INTEGRATE
}
cost_function_config {
target_classes {
name: “car”
class_weight: 1.0
coverage_foreground_weight: 0.05000000074505806
objectives {
name: “cov”
initial_weight: 1.0
weight_target: 1.0
}
objectives {
name: “bbox”
initial_weight: 10.0
weight_target: 10.0
}
}
target_classes {
name: “cyclist”
class_weight: 1.0
coverage_foreground_weight: 0.05000000074505806
objectives {
name: “cov”
initial_weight: 1.0
weight_target: 1.0
}
objectives {
name: “bbox”
initial_weight: 10.0
weight_target: 1.0
}
}
target_classes {
name: “pedestrian”
class_weight: 1.0
coverage_foreground_weight: 0.05000000074505806
objectives {
name: “cov”
initial_weight: 1.0
weight_target: 1.0
}
objectives {
name: “bbox”
initial_weight: 10.0
weight_target: 10.0
}
}
enable_autoweighting: true
max_objective_weight: 0.9998999834060669
min_objective_weight: 9.999999747378752e-05
}
training_config {
batch_size_per_gpu: 4
num_epochs: 1
enable_qat: False
learning_rate {
soft_start_annealing_schedule {
min_learning_rate: 4.999999873689376e-06
max_learning_rate: 0.0005000000237487257
soft_start: 0.10000000149011612
annealing: 0.699999988079071
}
}
regularizer {
type: L1
weight: 3.000000026176508e-09
}
optimizer {
adam {
epsilon: 9.99999993922529e-09
beta1: 0.8999999761581421
beta2: 0.9990000128746033
}
}
cost_scaling {
initial_exponent: 20.0
increment: 0.005
decrement: 1.0
}
visualizer {
enabled: true
num_images: 3
scalar_logging_frequency: 10
infrequent_logging_frequency: 1
target_class_config {
key: “car”
value {
coverage_threshold: 0.004999999888241291
}
}
target_class_config {
key: “pedestrian”
value {
coverage_threshold: 0.004999999888241291
}
}
}
}
bbox_rasterizer_config {
target_class_config {
key: “car”
value {
cov_center_x: 0.5
cov_center_y: 0.5
cov_radius_x: 0.4000000059604645
cov_radius_y: 0.4000000059604645
bbox_min_radius: 1.0
}
}
target_class_config {
key: “cyclist”
value {
cov_center_x: 0.5
cov_center_y: 0.5
cov_radius_x: 1.0
cov_radius_y: 1.0
bbox_min_radius: 1.0
}
}
target_class_config {
key: “pedestrian”
value {
cov_center_x: 0.5
cov_center_y: 0.5
cov_radius_x: 1.0
cov_radius_y: 1.0
bbox_min_radius: 1.0
}
}
deadzone_radius: 0.4000001549720764
}
)
• How to reproduce the issue ?
Inference code for TAO exported detectnet_v2 onnx model not available.
There is not inference code to run against onnx file by default. TAO provides code in tao_deploy/nvidia_tao_deploy/cv/detectnet_v2/inferencer.py at main · NVIDIA/tao_deploy · GitHub to run against tensorrt engine file. You may leverage it to write the code for running onnx. Any issue, please let me know.
sure. I will go through the code get back to you.