Hi,
I trained the detectnet_v2+Resnet18 on my custom dataset and I get a quite low precision with:
class name average precision (in %)
class1 53.7211
class2 46.899
The dataset contains also lots of small objects. I expect the low precision due to that. How can I tweak parameters to get better results on small objects also?
Here is my configuration file.
random_seed: 42
dataset_config {
data_sources {
tfrecords_path: "/workspace/tlt-experiments/tfrecords/kitti_trainval/*"
image_directory_path: "path"
}
image_extension: "jpg"
target_class_mapping {
key: "class1"
value: "class1"
}
target_class_mapping {
key: "class2"
value: "class2"
}
validation_fold: 0
}
augmentation_config {
preprocessing {
output_image_width: 608
output_image_height: 608
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: 8.0
translate_max_y: 8.0
}
color_augmentation {
hue_rotation_max: 25.0
saturation_shift_max: 0.20000000298
contrast_scale_max: 0.10000000149
contrast_center: 0.5
}
}
postprocessing_config {
target_class_config {
key: "class1"
value {
clustering_config {
coverage_threshold: 0.00499999988824
dbscan_eps: 0.15
dbscan_min_samples: 0.0500000007451
minimum_bounding_box_height: 1
}
}
}
target_class_config {
key: "class2"
value {
clustering_config {
coverage_threshold: 0.00499999988824
dbscan_eps: 0.15
dbscan_min_samples: 0.0500000007451
minimum_bounding_box_height: 1
}
}
}
}
}
model_config {
pretrained_model_file: "/workspace/tlt-experiments/pretrained_resnet18/tlt_resnet18_detectnet_v2_v1/resnet18.hdf5"
num_layers: 18
use_batch_norm: true
activation {
activation_type: "relu"
}
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: 1
minimum_detection_ground_truth_overlap {
key: "class1"
value: 0.5
}
minimum_detection_ground_truth_overlap {
key: "class2"
value: 0.5
}
evaluation_box_config {
key: "class1"
value {
minimum_height: 10
maximum_height: 9999
minimum_width: 5
maximum_width: 9999
}
}
evaluation_box_config {
key: "class2"
value {
minimum_height: 10
maximum_height: 9999
minimum_width: 5
maximum_width: 9999
}
}
average_precision_mode: INTEGRATE
}
cost_function_config {
target_classes {
name: "class1"
class_weight: 1.0
coverage_foreground_weight: 0.05
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: "class2"
class_weight: 1.0
coverage_foreground_weight: 0.05
objectives {
name: "cov"
initial_weight: 1.0
weight_target: 1.0
}
objectives {
name: "bbox"
initial_weight: 10.0
weight_target: 1.0
}
}
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: "class1"
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: "class2"
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
}
I was also wondering how to set initial_weight, weight_target? Is it essential?