Yes, here is my training spec sheet:
random_seed: 42
dataset_config {
data_sources {
tfrecords_path: "/workspace/tlt-experiments/data/tfrecords/kitti_trainval/*"
image_directory_path: "/workspace/tlt-experiments/data/training"
}
image_extension: "png"
target_class_mapping {
key: "low"
value: "low"
}
target_class_mapping {
key: "medium"
value: "medium"
}
target_class_mapping {
key: "high"
value: "high"
}
validation_fold: 0
}
augmentation_config {
preprocessing {
output_image_width: 704
output_image_height: 480
min_bbox_width: 1.0
min_bbox_height: 1.0
output_image_channel: 3
}
spatial_augmentation {
hflip_probability: 0.0
zoom_min: 1.0
zoom_max: 1.0
translate_max_x: 0.0
translate_max_y: 0.0
}
color_augmentation {
hue_rotation_max: 25.0
saturation_shift_max: 0.2
contrast_scale_max: 0.1
contrast_center: 0.5
}
}
postprocessing_config {
target_class_config {
key: "low"
value {
clustering_config {
coverage_threshold: 0.00499999988824
dbscan_eps: .7
dbscan_min_samples: 0.0500000007451
minimum_bounding_box_height: 75
}
}
}
target_class_config {
key: "medium"
value {
clustering_config {
coverage_threshold: 0.00499999988824
dbscan_eps: .7
dbscan_min_samples: 0.0500000007451
minimum_bounding_box_height: 75
}
}
}
target_class_config {
key: "high"
value {
clustering_config {
coverage_threshold: 0.00749999983236
dbscan_eps: .7
dbscan_min_samples: 0.0500000007451
minimum_bounding_box_height: 75
}
}
}
}
model_config {
pretrained_model_file: "/workspace/tlt-experiments/detectnet_v2/pretrained_resnet18/tlt_pretrained_detectnet_v2_vresnet18/resnet18.hdf5"
num_layers: 18
use_batch_norm: true
objective_set {
bbox {
scale: 35.0
offset: 0.5
}
cov {
}
}
training_precision {
backend_floatx: FLOAT32
}
arch: "resnet"
}
evaluation_config {
validation_period_during_training: 10
first_validation_epoch: 30
minimum_detection_ground_truth_overlap {
key: "low"
value: 0.80
}
minimum_detection_ground_truth_overlap {
key: "medium"
value: 0.80
}
minimum_detection_ground_truth_overlap {
key: "high"
value: 0.80
}
evaluation_box_config {
key: "low"
value {
minimum_height: 10
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
evaluation_box_config {
key: "medium"
value {
minimum_height: 10
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
evaluation_box_config {
key: "high"
value {
minimum_height: 10
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
average_precision_mode: INTEGRATE
}
cost_function_config {
target_classes {
name: "low"
class_weight: 2.22
coverage_foreground_weight: 0.0500000007451
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: "medium"
class_weight: 3.49
coverage_foreground_weight: 0.0500000007451
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: "high"
class_weight: 3.77
coverage_foreground_weight: 0.0500000007451
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: 0.999899983406
min_objective_weight: 9.99999974738e-05
}
training_config {
batch_size_per_gpu: 4
num_epochs: 120
learning_rate {
soft_start_annealing_schedule {
min_learning_rate: 5e-06
max_learning_rate: 5e-04
soft_start: 0.10000000149
annealing: 0.699999988079
}
}
regularizer {
type: L1
weight: 3.00000002618e-09
}
optimizer {
adam {
epsilon: 9.99999993923e-09
beta1: 0.899999976158
beta2: 0.999000012875
}
}
cost_scaling {
initial_exponent: 20.0
increment: 0.005
decrement: 1.0
}
checkpoint_interval: 10
}
bbox_rasterizer_config {
target_class_config {
key: "low"
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: "medium"
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: "high"
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: 0.400000154972
}
And here is my inference spec sheet used in the Jupyter Notebook:
inferencer_config{
# defining target class names for the experiment.
# Note: This must be mentioned in order of the networks classes.
target_classes: "low"
target_classes: "medium"
target_classes: "high"
# Inference dimensions.
image_width: 704
image_height: 480
# Must match what the model was trained for.
image_channels: 3
batch_size: 8
gpu_index: 0
# model handler config
tlt_config{
model: "/workspace/tlt-experiments/detectnet_v2/experiment_dir_retrain/weights/resnet18_detector_pruned.tlt"
}
}
bbox_handler_config{
kitti_dump: true
disable_overlay: false
overlay_linewidth: 2
classwise_bbox_handler_config{
key:"low"
value: {
confidence_model: "aggregate_cov"
output_map: "low"
confidence_threshold: 100.0
bbox_color{
R: 255
G: 0
B: 0
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: .7
dbscan_min_samples: 0.05
minimum_bounding_box_height: 4
}
}
}
classwise_bbox_handler_config{
key:"medium"
value: {
confidence_model: "aggregate_cov"
output_map: "medium"
confidence_threshold: 100.0
bbox_color{
R: 0
G: 255
B: 0
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: .7
dbscan_min_samples: 0.05
minimum_bounding_box_height: 4
}
}
}
classwise_bbox_handler_config{
key:"high"
value: {
confidence_model: "aggregate_cov"
output_map: "high"
confidence_threshold: 100.0
bbox_color{
R: 0
G: 0
B: 255
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: .7
dbscan_min_samples: 0.05
minimum_bounding_box_height: 4
}
}
}
classwise_bbox_handler_config{
key:"default"
value: {
confidence_model: "aggregate_cov"
confidence_threshold: 100.0
bbox_color{
R: 255
G: 255
B: 0
}
clustering_config{
coverage_threshold: 0.00
dbscan_eps: .7
dbscan_min_samples: 0.05
minimum_bounding_box_height: 4
}
}
}
}
My model does very well in inference using the aggregate_cov model, but poorly using the mean_cov model. I’m trying to replicate these parameters in the deepstream spec sheets, but I’m not able to achieve the same results. My labels file is also set up the same way in the example.
