Getting 60% mAP with 25 epochs in AutoML vs. 43% with 100 epochs without AutoML, using the same spec

Please provide the following information when requesting support.

• Hardware T4
• Network Type Detectnet_v2
• Training spec file(This gave the best results)
random_seed: 42
dataset_config {
data_sources {
tfrecords_path: “/shared/users/3ce93a7c-0889-537e-8991-7d76b64a7194/datasets/1dc8724f-92cc-4cdf-84cc-be752286a711/tfrecords/"
image_directory_path: “/shared/users/3ce93a7c-0889-537e-8991-7d76b64a7194/datasets/1dc8724f-92cc-4cdf-84cc-be752286a711/”
}
image_extension: “jpg”
target_class_mapping {
key: “face”
value: “face”
}
target_class_mapping {
key: “person”
value: “person”
}
validation_data_source {
tfrecords_path: "/shared/users/3ce93a7c-0889-537e-8991-7d76b64a7194/datasets/d07bc44b-9824-479c-a946-da65d24fe5ca/tfrecords/

image_directory_path: “/shared/users/3ce93a7c-0889-537e-8991-7d76b64a7194/datasets/d07bc44b-9824-479c-a946-da65d24fe5ca/”
}
}
augmentation_config {
preprocessing {
output_image_width: 960
output_image_height: 544
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: “face”
value {
clustering_config {
coverage_threshold: 0.007499999832361937
minimum_bounding_box_height: 20
dbscan_eps: 0.23000000417232513
dbscan_min_samples: 1
dbscan_confidence_threshold: 0.7200000286102295
nms_iou_threshold: 0.20000000298023224
}
}
}
target_class_config {
key: “person”
value {
clustering_config {
coverage_threshold: 0.007499999832361937
minimum_bounding_box_height: 20
dbscan_eps: 0.23000000417232513
dbscan_min_samples: 1
dbscan_confidence_threshold: 0.10000000149011612
nms_iou_threshold: 0.20000000298023224
}
}
}
}
model_config {
pretrained_model_file: “/shared/users/3ce93a7c-0889-537e-8991-7d76b64a7194/models/86b93c2e-70a1-42bb-aa1d-d0e1349c4adc/peoplenet_vtrainable_v2.6/resnet34_peoplenet.tlt”
num_layers: 34
use_batch_norm: true
objective_set {
bbox {
scale: 35.0
offset: 0.5
}
cov {
}
}
arch: “resnet”
}
evaluation_config {
validation_period_during_training: 1
first_validation_epoch: 1
minimum_detection_ground_truth_overlap {
key: “face”
value: 0.5
}
minimum_detection_ground_truth_overlap {
key: “person”
value: 0.5
}
evaluation_box_config {
key: “face”
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
evaluation_box_config {
key: “person”
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
}
cost_function_config {
target_classes {
name: “person”
class_weight: 4.0
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: “face”
class_weight: 4.0
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: 25
learning_rate {
soft_start_annealing_schedule {
min_learning_rate: 3.5999998999614036e-06
max_learning_rate: 0.0005000000237487257
soft_start: 1.0000000116860974e-07
annealing: 1.0000000116860974e-07
}
}
regularizer {
type: L2
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
}
checkpoint_interval: 1
visualizer {
num_images: 3
infrequent_logging_frequency: 5
}
}
bbox_rasterizer_config {
target_class_config {
key: “face”
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: “person”
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: 0.4000000059604645
}

• Status.json file for the training with automl=false:

{“date”: “3/18/2024”, “time”: “5:29:7”, “status”: “STARTED”, “verbosity”: “INFO”, “message”: “Starting DetectNet_v2 Training job”}

{“date”: “3/18/2024”, “time”: “5:29:7”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Training gridbox model.”}

{“date”: “3/18/2024”, “time”: “5:29:7”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Building DetectNet V2 model”}

{“date”: “3/18/2024”, “time”: “5:29:52”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “DetectNet V2 model built.”}

{“date”: “3/18/2024”, “time”: “5:29:52”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Building rasterizer.”}

{“date”: “3/18/2024”, “time”: “5:29:52”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Rasterizers built.”}

{“date”: “3/18/2024”, “time”: “5:29:52”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Building training graph.”}

{“date”: “3/18/2024”, “time”: “5:29:59”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Rasterizing tensors.”}

{“date”: “3/18/2024”, “time”: “5:29:59”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Tensors rasterized.”}

{“date”: “3/18/2024”, “time”: “5:30:2”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Training graph built.”}

{“date”: “3/18/2024”, “time”: “5:30:2”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Building validation graph.”}

{“date”: “3/18/2024”, “time”: “5:30:3”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Rasterizing tensors.”}

{“date”: “3/18/2024”, “time”: “5:30:3”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Tensors rasterized.”}

{“date”: “3/18/2024”, “time”: “5:30:3”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Validation graph built.”}

{“date”: “3/18/2024”, “time”: “5:30:5”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Running training loop.”}

{“epoch”: 0, “max_epoch”: 100, “time_per_epoch”: “0:00:00”, “eta”: “0:00:00”, “date”: “3/18/2024”, “time”: “5:31:31”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.12350214272737503, “learning_rate”: “3.600001e-06”}}

{“epoch”: 1, “max_epoch”: 100, “time_per_epoch”: “0:05:26.528986”, “eta”: “8:58:46.369612”, “date”: “3/18/2024”, “time”: “5:36:36”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0052212937735021114, “learning_rate”: “0.0004759306”}}

{“epoch”: 2, “max_epoch”: 100, “time_per_epoch”: “0:04:54.824014”, “eta”: “8:01:32.753344”, “date”: “3/18/2024”, “time”: “5:41:31”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0048576844856143, “learning_rate”: “0.0004530195”}}

{“epoch”: 3, “max_epoch”: 100, “time_per_epoch”: “0:04:54.802147”, “eta”: “7:56:35.808227”, “date”: “3/18/2024”, “time”: “5:46:26”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.004707717336714268, “learning_rate”: “0.0004312115”}}

{“epoch”: 4, “max_epoch”: 100, “time_per_epoch”: “0:04:54.752106”, “eta”: “7:51:36.202217”, “date”: “3/18/2024”, “time”: “5:51:20”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.004509217571467161, “learning_rate”: “0.00041045295”}}

{“epoch”: 5, “max_epoch”: 100, “time_per_epoch”: “0:04:54.422673”, “eta”: “7:46:10.153888”, “date”: “3/18/2024”, “time”: “5:56:15”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.004182483069598675, “learning_rate”: “0.00039069407”}}

{“epoch”: 6, “max_epoch”: 100, “time_per_epoch”: “0:04:54.160523”, “eta”: “7:40:51.089179”, “date”: “3/18/2024”, “time”: “6:1:9”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0038193000946193933, “learning_rate”: “0.0003718862”}}

{“epoch”: 7, “max_epoch”: 100, “time_per_epoch”: “0:04:54.105846”, “eta”: “7:35:51.843649”, “date”: “3/18/2024”, “time”: “6:6:3”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0035149625036865473, “learning_rate”: “0.00035398392”}}

{“epoch”: 8, “max_epoch”: 100, “time_per_epoch”: “0:04:54.031194”, “eta”: “7:30:50.869889”, “date”: “3/18/2024”, “time”: “6:10:57”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0033179381862282753, “learning_rate”: “0.00033694328”}}

{“epoch”: 9, “max_epoch”: 100, “time_per_epoch”: “0:04:54.015070”, “eta”: “7:25:55.371345”, “date”: “3/18/2024”, “time”: “6:15:51”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0032990146428346634, “learning_rate”: “0.00032072281”}}

{“epoch”: 10, “max_epoch”: 100, “time_per_epoch”: “0:04:53.937594”, “eta”: “7:20:54.383454”, “date”: “3/18/2024”, “time”: “6:20:45”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0030838842503726482, “learning_rate”: “0.0003052838”}}

{“epoch”: 11, “max_epoch”: 100, “time_per_epoch”: “0:04:53.970156”, “eta”: “7:16:03.343901”, “date”: “3/18/2024”, “time”: “6:25:39”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0028885009232908487, “learning_rate”: “0.0002905874”}}

{“epoch”: 12, “max_epoch”: 100, “time_per_epoch”: “0:04:53.936373”, “eta”: “7:11:06.400803”, “date”: “3/18/2024”, “time”: “6:30:33”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0029576399829238653, “learning_rate”: “0.00027659873”}}

{“epoch”: 13, “max_epoch”: 100, “time_per_epoch”: “0:04:53.657555”, “eta”: “7:05:48.207273”, “date”: “3/18/2024”, “time”: “6:35:26”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.002731648040935397, “learning_rate”: “0.00026328352”}}

{“epoch”: 14, “max_epoch”: 100, “time_per_epoch”: “0:04:53.470560”, “eta”: “7:00:38.468125”, “date”: “3/18/2024”, “time”: “6:40:20”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0028078609611839056, “learning_rate”: “0.00025060904”}}

{“epoch”: 15, “max_epoch”: 100, “time_per_epoch”: “0:04:53.603970”, “eta”: “6:55:56.337475”, “date”: “3/18/2024”, “time”: “6:45:14”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0027474015951156616, “learning_rate”: “0.00023854492”}}

{“epoch”: 16, “max_epoch”: 100, “time_per_epoch”: “0:04:53.789315”, “eta”: “6:51:18.302459”, “date”: “3/18/2024”, “time”: “6:50:7”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0027728599961847067, “learning_rate”: “0.00022706158”}}

{“epoch”: 17, “max_epoch”: 100, “time_per_epoch”: “0:04:53.727452”, “eta”: “6:46:19.378539”, “date”: “3/18/2024”, “time”: “6:55:1”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.002883307170122862, “learning_rate”: “0.00021613082”}}

{“epoch”: 18, “max_epoch”: 100, “time_per_epoch”: “0:04:53.584270”, “eta”: “6:41:13.910140”, “date”: “3/18/2024”, “time”: “6:59:55”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.002710917731747031, “learning_rate”: “0.00020572645”}}

{“epoch”: 19, “max_epoch”: 100, “time_per_epoch”: “0:04:53.911009”, “eta”: “6:36:46.791696”, “date”: “3/18/2024”, “time”: “7:4:49”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0030504981987178326, “learning_rate”: “0.00019582297”}}

{“epoch”: 20, “max_epoch”: 100, “time_per_epoch”: “0:04:53.773967”, “eta”: “6:31:41.917362”, “date”: “3/18/2024”, “time”: “7:9:42”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0027212565764784813, “learning_rate”: “0.00018639604”}}

{“epoch”: 21, “max_epoch”: 100, “time_per_epoch”: “0:04:53.501610”, “eta”: “6:26:26.627193”, “date”: “3/18/2024”, “time”: “7:14:36”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0027916438411921263, “learning_rate”: “0.00017742308”}}

{“epoch”: 22, “max_epoch”: 100, “time_per_epoch”: “0:04:53.497208”, “eta”: “6:21:32.782233”, “date”: “3/18/2024”, “time”: “7:19:29”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.002889100695028901, “learning_rate”: “0.00016888208”}}

{“epoch”: 23, “max_epoch”: 100, “time_per_epoch”: “0:04:53.514034”, “eta”: “6:16:40.580602”, “date”: “3/18/2024”, “time”: “7:24:23”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.003093051491305232, “learning_rate”: “0.00016075208”}}

{“epoch”: 24, “max_epoch”: 100, “time_per_epoch”: “0:04:53.646533”, “eta”: “6:11:57.136473”, “date”: “3/18/2024”, “time”: “7:29:17”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0027831881307065487, “learning_rate”: “0.00015301362”}}

{“epoch”: 25, “max_epoch”: 100, “time_per_epoch”: “0:04:53.588736”, “eta”: “6:06:59.155186”, “date”: “3/18/2024”, “time”: “7:34:10”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0030726115219295025, “learning_rate”: “0.00014564766”}}

{“epoch”: 26, “max_epoch”: 100, “time_per_epoch”: “0:04:53.502835”, “eta”: “6:01:59.209793”, “date”: “3/18/2024”, “time”: “7:39:4”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0029167369939386845, “learning_rate”: “0.00013863618”}}

{“epoch”: 27, “max_epoch”: 100, “time_per_epoch”: “0:04:53.489785”, “eta”: “5:57:04.754302”, “date”: “3/18/2024”, “time”: “7:43:57”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.002933724783360958, “learning_rate”: “0.00013196236”}}

{“epoch”: 28, “max_epoch”: 100, “time_per_epoch”: “0:04:53.547155”, “eta”: “5:52:15.395136”, “date”: “3/18/2024”, “time”: “7:48:51”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0028260110411792994, “learning_rate”: “0.00012560979”}}

{“epoch”: 29, “max_epoch”: 100, “time_per_epoch”: “0:04:53.638603”, “eta”: “5:47:28.340811”, “date”: “3/18/2024”, “time”: “7:53:44”, “status”: “RUNNING”, “verbosity”: “INFO”, “graphical”: {“loss”: 0.0030957330018281937, “learning_rate”: “0.00011956293”}}

{“date”: “3/18/2024”, “time”: “8:0:6”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Evaluation metrics generated.”, “categorical”: {“average_precision”: {“person”: 0.1336, “face”: 0.0}}, “graphical”: {“loss”: 0.0030957330018281937, “learning_rate”: “0.00011956293”}, “kpi”: {“validation cost”: 0.02069363, “mean average precision”: 0.0668}}

{“epoch”: 30, “max_epoch”: 100, “time_per_epoch”: “0:06:21.842062”, “eta”: “7:25:28.944306”, “date”: “3/18/2024”, “time”: “8:0:6”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.1336, “face”: 0.0}}, “graphical”: {“loss”: 0.0030171480029821396, “learning_rate”: “0.00011380728”}, “kpi”: {“validation cost”: 0.02069363, “mean average precision”: 0.0668}}

{“epoch”: 31, “max_epoch”: 100, “time_per_epoch”: “0:04:52.864593”, “eta”: “5:36:47.656919”, “date”: “3/18/2024”, “time”: “8:4:59”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.1336, “face”: 0.0}}, “graphical”: {“loss”: 0.003068220801651478, “learning_rate”: “0.000108328684”}, “kpi”: {“validation cost”: 0.02069363, “mean average precision”: 0.0668}}

{“epoch”: 32, “max_epoch”: 100, “time_per_epoch”: “0:04:53.309618”, “eta”: “5:32:25.054056”, “date”: “3/18/2024”, “time”: “8:9:52”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.1336, “face”: 0.0}}, “graphical”: {“loss”: 0.0029432999435812235, “learning_rate”: “0.000103113736”}, “kpi”: {“validation cost”: 0.02069363, “mean average precision”: 0.0668}}

{“epoch”: 33, “max_epoch”: 100, “time_per_epoch”: “0:04:53.233032”, “eta”: “5:27:26.613159”, “date”: “3/18/2024”, “time”: “8:14:46”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.1336, “face”: 0.0}}, “graphical”: {“loss”: 0.0031127554830163717, “learning_rate”: “9.814983e-05”}, “kpi”: {“validation cost”: 0.02069363, “mean average precision”: 0.0668}}

{“epoch”: 34, “max_epoch”: 100, “time_per_epoch”: “0:04:53.388427”, “eta”: “5:22:43.636215”, “date”: “3/18/2024”, “time”: “8:19:39”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.1336, “face”: 0.0}}, “graphical”: {“loss”: 0.003155471757054329, “learning_rate”: “9.342498e-05”}, “kpi”: {“validation cost”: 0.02069363, “mean average precision”: 0.0668}}

{“epoch”: 35, “max_epoch”: 100, “time_per_epoch”: “0:04:53.507377”, “eta”: “5:17:57.979499”, “date”: “3/18/2024”, “time”: “8:24:32”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.1336, “face”: 0.0}}, “graphical”: {“loss”: 0.003031008644029498, “learning_rate”: “8.892758e-05”}, “kpi”: {“validation cost”: 0.02069363, “mean average precision”: 0.0668}}

{“epoch”: 36, “max_epoch”: 100, “time_per_epoch”: “0:04:53.565197”, “eta”: “5:13:08.172638”, “date”: “3/18/2024”, “time”: “8:29:26”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.1336, “face”: 0.0}}, “graphical”: {“loss”: 0.0031345996540039778, “learning_rate”: “8.464668e-05”}, “kpi”: {“validation cost”: 0.02069363, “mean average precision”: 0.0668}}

{“epoch”: 37, “max_epoch”: 100, “time_per_epoch”: “0:04:53.467636”, “eta”: “5:08:08.461045”, “date”: “3/18/2024”, “time”: “8:34:19”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.1336, “face”: 0.0}}, “graphical”: {“loss”: 0.0030491002835333347, “learning_rate”: “8.057187e-05”}, “kpi”: {“validation cost”: 0.02069363, “mean average precision”: 0.0668}}

{“epoch”: 38, “max_epoch”: 100, “time_per_epoch”: “0:04:53.274091”, “eta”: “5:03:02.993642”, “date”: “3/18/2024”, “time”: “8:39:13”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.1336, “face”: 0.0}}, “graphical”: {“loss”: 0.003231216687709093, “learning_rate”: “7.6693126e-05”}, “kpi”: {“validation cost”: 0.02069363, “mean average precision”: 0.0668}}

{“epoch”: 39, “max_epoch”: 100, “time_per_epoch”: “0:04:53.397132”, “eta”: “4:58:17.225062”, “date”: “3/18/2024”, “time”: “8:44:6”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.1336, “face”: 0.0}}, “graphical”: {“loss”: 0.0031119724735617638, “learning_rate”: “7.300118e-05”}, “kpi”: {“validation cost”: 0.02069363, “mean average precision”: 0.0668}}

{“date”: “3/18/2024”, “time”: “8:50:53”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Evaluation metrics generated.”, “categorical”: {“average_precision”: {“person”: 0.4065, “face”: 0.0}}, “graphical”: {“loss”: 0.0031119724735617638, “learning_rate”: “7.300118e-05”}, “kpi”: {“validation cost”: 0.00555729, “mean average precision”: 0.2032}}

{“epoch”: 40, “max_epoch”: 100, “time_per_epoch”: “0:06:47.435009”, “eta”: “6:47:26.100540”, “date”: “3/18/2024”, “time”: “8:50:54”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.4065, “face”: 0.0}}, “graphical”: {“loss”: 0.0031916098669171333, “learning_rate”: “6.9486974e-05”}, “kpi”: {“validation cost”: 0.00555729, “mean average precision”: 0.2032}}

{“epoch”: 41, “max_epoch”: 100, “time_per_epoch”: “0:04:52.794801”, “eta”: “4:47:54.893273”, “date”: “3/18/2024”, “time”: “8:55:46”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.4065, “face”: 0.0}}, “graphical”: {“loss”: 0.003139785025268793, “learning_rate”: “6.614186e-05”}, “kpi”: {“validation cost”: 0.00555729, “mean average precision”: 0.2032}}

{“epoch”: 42, “max_epoch”: 100, “time_per_epoch”: “0:04:53.564847”, “eta”: “4:43:46.761153”, “date”: “3/18/2024”, “time”: “9:0:40”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.4065, “face”: 0.0}}, “graphical”: {“loss”: 0.0034749582409858704, “learning_rate”: “6.295785e-05”}, “kpi”: {“validation cost”: 0.00555729, “mean average precision”: 0.2032}}

{“epoch”: 43, “max_epoch”: 100, “time_per_epoch”: “0:04:53.349640”, “eta”: “4:38:40.929474”, “date”: “3/18/2024”, “time”: “9:5:33”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.4065, “face”: 0.0}}, “graphical”: {“loss”: 0.003363420255482197, “learning_rate”: “5.992711e-05”}, “kpi”: {“validation cost”: 0.00555729, “mean average precision”: 0.2032}}

{“epoch”: 44, “max_epoch”: 100, “time_per_epoch”: “0:04:53.194854”, “eta”: “4:33:38.911798”, “date”: “3/18/2024”, “time”: “9:10:26”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.4065, “face”: 0.0}}, “graphical”: {“loss”: 0.0033171381801366806, “learning_rate”: “5.7042216e-05”}, “kpi”: {“validation cost”: 0.00555729, “mean average precision”: 0.2032}}

{“epoch”: 45, “max_epoch”: 100, “time_per_epoch”: “0:04:53.362448”, “eta”: “4:28:54.934652”, “date”: “3/18/2024”, “time”: “9:15:20”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.4065, “face”: 0.0}}, “graphical”: {“loss”: 0.0031434076372534037, “learning_rate”: “5.429625e-05”}, “kpi”: {“validation cost”: 0.00555729, “mean average precision”: 0.2032}}

{“epoch”: 46, “max_epoch”: 100, “time_per_epoch”: “0:04:53.273128”, “eta”: “4:23:56.748901”, “date”: “3/18/2024”, “time”: “9:20:13”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.4065, “face”: 0.0}}, “graphical”: {“loss”: 0.0031344096641987562, “learning_rate”: “5.1682473e-05”}, “kpi”: {“validation cost”: 0.00555729, “mean average precision”: 0.2032}}

{“epoch”: 47, “max_epoch”: 100, “time_per_epoch”: “0:04:53.342000”, “eta”: “4:19:07.125975”, “date”: “3/18/2024”, “time”: “9:25:6”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.4065, “face”: 0.0}}, “graphical”: {“loss”: 0.0031619195360690355, “learning_rate”: “4.9194474e-05”}, “kpi”: {“validation cost”: 0.00555729, “mean average precision”: 0.2032}}

{“epoch”: 48, “max_epoch”: 100, “time_per_epoch”: “0:04:53.399560”, “eta”: “4:14:16.777143”, “date”: “3/18/2024”, “time”: “9:30:0”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.4065, “face”: 0.0}}, “graphical”: {“loss”: 0.003419755259528756, “learning_rate”: “4.6826295e-05”}, “kpi”: {“validation cost”: 0.00555729, “mean average precision”: 0.2032}}

{“epoch”: 49, “max_epoch”: 100, “time_per_epoch”: “0:04:53.249457”, “eta”: “4:09:15.722301”, “date”: “3/18/2024”, “time”: “9:34:53”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.4065, “face”: 0.0}}, “graphical”: {“loss”: 0.0033195791766047478, “learning_rate”: “4.4572116e-05”}, “kpi”: {“validation cost”: 0.00555729, “mean average precision”: 0.2032}}

{“date”: “3/18/2024”, “time”: “9:41:56”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Evaluation metrics generated.”, “categorical”: {“average_precision”: {“person”: 0.6679, “face”: 0.0}}, “graphical”: {“loss”: 0.0033195791766047478, “learning_rate”: “4.4572116e-05”}, “kpi”: {“validation cost”: 0.0042146, “mean average precision”: 0.3339}}

{“epoch”: 50, “max_epoch”: 100, “time_per_epoch”: “0:07:03.749731”, “eta”: “5:53:07.486529”, “date”: “3/18/2024”, “time”: “9:41:57”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.6679, “face”: 0.0}}, “graphical”: {“loss”: 0.003534146351739764, “learning_rate”: “4.242641e-05”}, “kpi”: {“validation cost”: 0.0042146, “mean average precision”: 0.3339}}

{“epoch”: 51, “max_epoch”: 100, “time_per_epoch”: “0:04:52.458383”, “eta”: “3:58:50.460759”, “date”: “3/18/2024”, “time”: “9:46:49”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.6679, “face”: 0.0}}, “graphical”: {“loss”: 0.003287079045549035, “learning_rate”: “4.038404e-05”}, “kpi”: {“validation cost”: 0.0042146, “mean average precision”: 0.3339}}

{“epoch”: 52, “max_epoch”: 100, “time_per_epoch”: “0:04:53.121826”, “eta”: “3:54:29.847656”, “date”: “3/18/2024”, “time”: “9:51:42”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.6679, “face”: 0.0}}, “graphical”: {“loss”: 0.0033090091310441494, “learning_rate”: “3.8439986e-05”}, “kpi”: {“validation cost”: 0.0042146, “mean average precision”: 0.3339}}

{“epoch”: 53, “max_epoch”: 100, “time_per_epoch”: “0:04:53.119769”, “eta”: “3:49:36.629125”, “date”: “3/18/2024”, “time”: “9:56:36”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.6679, “face”: 0.0}}, “graphical”: {“loss”: 0.0034158003982156515, “learning_rate”: “3.658948e-05”}, “kpi”: {“validation cost”: 0.0042146, “mean average precision”: 0.3339}}

{“epoch”: 54, “max_epoch”: 100, “time_per_epoch”: “0:04:52.907057”, “eta”: “3:44:33.724635”, “date”: “3/18/2024”, “time”: “10:1:29”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.6679, “face”: 0.0}}, “graphical”: {“loss”: 0.003295001108199358, “learning_rate”: “3.4828096e-05”}, “kpi”: {“validation cost”: 0.0042146, “mean average precision”: 0.3339}}

{“epoch”: 55, “max_epoch”: 100, “time_per_epoch”: “0:04:53.056832”, “eta”: “3:39:47.557454”, “date”: “3/18/2024”, “time”: “10:6:22”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.6679, “face”: 0.0}}, “graphical”: {“loss”: 0.003340038238093257, “learning_rate”: “3.31515e-05”}, “kpi”: {“validation cost”: 0.0042146, “mean average precision”: 0.3339}}

{“epoch”: 56, “max_epoch”: 100, “time_per_epoch”: “0:04:53.138070”, “eta”: “3:34:58.075095”, “date”: “3/18/2024”, “time”: “10:11:15”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.6679, “face”: 0.0}}, “graphical”: {“loss”: 0.003355278167873621, “learning_rate”: “3.1555584e-05”}, “kpi”: {“validation cost”: 0.0042146, “mean average precision”: 0.3339}}

{“epoch”: 57, “max_epoch”: 100, “time_per_epoch”: “0:04:53.162398”, “eta”: “3:30:05.983108”, “date”: “3/18/2024”, “time”: “10:16:8”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.6679, “face”: 0.0}}, “graphical”: {“loss”: 0.003445375245064497, “learning_rate”: “3.0036525e-05”}, “kpi”: {“validation cost”: 0.0042146, “mean average precision”: 0.3339}}

{“epoch”: 58, “max_epoch”: 100, “time_per_epoch”: “0:04:53.003401”, “eta”: “3:25:06.142824”, “date”: “3/18/2024”, “time”: “10:21:1”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.6679, “face”: 0.0}}, “graphical”: {“loss”: 0.003332550171762705, “learning_rate”: “2.8590592e-05”}, “kpi”: {“validation cost”: 0.0042146, “mean average precision”: 0.3339}}

{“epoch”: 59, “max_epoch”: 100, “time_per_epoch”: “0:04:52.982104”, “eta”: “3:20:12.266257”, “date”: “3/18/2024”, “time”: “10:25:54”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 0.6679, “face”: 0.0}}, “graphical”: {“loss”: 0.0036308341659605503, “learning_rate”: “2.721424e-05”}, “kpi”: {“validation cost”: 0.0042146, “mean average precision”: 0.3339}}

{“date”: “3/18/2024”, “time”: “10:32:55”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Evaluation metrics generated.”, “categorical”: {“average_precision”: {“person”: 3.0264, “face”: 0.0}}, “graphical”: {“loss”: 0.0036308341659605503, “learning_rate”: “2.721424e-05”}, “kpi”: {“validation cost”: 0.00420063, “mean average precision”: 1.5132}}

{“epoch”: 60, “max_epoch”: 100, “time_per_epoch”: “0:07:01.079051”, “eta”: “4:40:43.162031”, “date”: “3/18/2024”, “time”: “10:32:55”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 3.0264, “face”: 0.0}}, “graphical”: {“loss”: 0.0033476483076810837, “learning_rate”: “2.5904168e-05”}, “kpi”: {“validation cost”: 0.00420063, “mean average precision”: 1.5132}}

{“epoch”: 61, “max_epoch”: 100, “time_per_epoch”: “0:04:52.432060”, “eta”: “3:10:04.850322”, “date”: “3/18/2024”, “time”: “10:37:47”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 3.0264, “face”: 0.0}}, “graphical”: {“loss”: 0.003582376055419445, “learning_rate”: “2.4657164e-05”}, “kpi”: {“validation cost”: 0.00420063, “mean average precision”: 1.5132}}

{“epoch”: 62, “max_epoch”: 100, “time_per_epoch”: “0:04:52.916453”, “eta”: “3:05:30.825219”, “date”: “3/18/2024”, “time”: “10:42:40”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 3.0264, “face”: 0.0}}, “graphical”: {“loss”: 0.0033375455532222986, “learning_rate”: “2.3470167e-05”}, “kpi”: {“validation cost”: 0.00420063, “mean average precision”: 1.5132}}

{“epoch”: 63, “max_epoch”: 100, “time_per_epoch”: “0:04:53.147115”, “eta”: “3:00:46.443237”, “date”: “3/18/2024”, “time”: “10:47:33”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 3.0264, “face”: 0.0}}, “graphical”: {“loss”: 0.0034082632046192884, “learning_rate”: “2.2340331e-05”}, “kpi”: {“validation cost”: 0.00420063, “mean average precision”: 1.5132}}

{“epoch”: 64, “max_epoch”: 100, “time_per_epoch”: “0:04:52.999289”, “eta”: “2:55:47.974414”, “date”: “3/18/2024”, “time”: “10:52:26”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 3.0264, “face”: 0.0}}, “graphical”: {“loss”: 0.0033140622545033693, “learning_rate”: “2.1264885e-05”}, “kpi”: {“validation cost”: 0.00420063, “mean average precision”: 1.5132}}

{“epoch”: 65, “max_epoch”: 100, “time_per_epoch”: “0:04:53.205723”, “eta”: “2:51:02.200298”, “date”: “3/18/2024”, “time”: “10:57:20”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 3.0264, “face”: 0.0}}, “graphical”: {“loss”: 0.0033930777572095394, “learning_rate”: “2.0241192e-05”}, “kpi”: {“validation cost”: 0.00420063, “mean average precision”: 1.5132}}

{“epoch”: 66, “max_epoch”: 100, “time_per_epoch”: “0:04:53.269646”, “eta”: “2:46:11.167970”, “date”: “3/18/2024”, “time”: “11:2:13”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 3.0264, “face”: 0.0}}, “graphical”: {“loss”: 0.003599527757614851, “learning_rate”: “1.92668e-05”}, “kpi”: {“validation cost”: 0.00420063, “mean average precision”: 1.5132}}

{“epoch”: 67, “max_epoch”: 100, “time_per_epoch”: “0:04:52.961608”, “eta”: “2:41:07.733070”, “date”: “3/18/2024”, “time”: “11:7:6”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 3.0264, “face”: 0.0}}, “graphical”: {“loss”: 0.003385691437870264, “learning_rate”: “1.8339311e-05”}, “kpi”: {“validation cost”: 0.00420063, “mean average precision”: 1.5132}}

{“epoch”: 68, “max_epoch”: 100, “time_per_epoch”: “0:04:53.036511”, “eta”: “2:36:17.168365”, “date”: “3/18/2024”, “time”: “11:11:59”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 3.0264, “face”: 0.0}}, “graphical”: {“loss”: 0.0036833591293543577, “learning_rate”: “1.7456456e-05”}, “kpi”: {“validation cost”: 0.00420063, “mean average precision”: 1.5132}}

{“epoch”: 69, “max_epoch”: 100, “time_per_epoch”: “0:04:53.265760”, “eta”: “2:31:31.238551”, “date”: “3/18/2024”, “time”: “11:16:52”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 3.0264, “face”: 0.0}}, “graphical”: {“loss”: 0.003437579842284322, “learning_rate”: “1.6616115e-05”}, “kpi”: {“validation cost”: 0.00420063, “mean average precision”: 1.5132}}

{“date”: “3/18/2024”, “time”: “11:23:26”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Evaluation metrics generated.”, “categorical”: {“average_precision”: {“person”: 6.439, “face”: 9.0909}}, “graphical”: {“loss”: 0.003437579842284322, “learning_rate”: “1.6616115e-05”}, “kpi”: {“validation cost”: 0.00139689, “mean average precision”: 7.765}}

{“epoch”: 70, “max_epoch”: 100, “time_per_epoch”: “0:06:33.969041”, “eta”: “3:16:59.071219”, “date”: “3/18/2024”, “time”: “11:23:26”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 6.439, “face”: 9.0909}}, “graphical”: {“loss”: 0.003620726056396961, “learning_rate”: “1.5816231e-05”}, “kpi”: {“validation cost”: 0.00139689, “mean average precision”: 7.765}}

{“epoch”: 71, “max_epoch”: 100, “time_per_epoch”: “0:04:52.587596”, “eta”: “2:21:25.040275”, “date”: “3/18/2024”, “time”: “11:28:19”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 6.439, “face”: 9.0909}}, “graphical”: {“loss”: 0.0034732164349406958, “learning_rate”: “1.5054837e-05”}, “kpi”: {“validation cost”: 0.00139689, “mean average precision”: 7.765}}

{“epoch”: 72, “max_epoch”: 100, “time_per_epoch”: “0:04:53.067363”, “eta”: “2:16:45.886171”, “date”: “3/18/2024”, “time”: “11:33:12”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 6.439, “face”: 9.0909}}, “graphical”: {“loss”: 0.0035520775709301233, “learning_rate”: “1.4330109e-05”}, “kpi”: {“validation cost”: 0.00139689, “mean average precision”: 7.765}}

{“epoch”: 73, “max_epoch”: 100, “time_per_epoch”: “0:04:52.833707”, “eta”: “2:11:46.510085”, “date”: “3/18/2024”, “time”: “11:38:5”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 6.439, “face”: 9.0909}}, “graphical”: {“loss”: 0.0033709092531353235, “learning_rate”: “1.36402705e-05”}, “kpi”: {“validation cost”: 0.00139689, “mean average precision”: 7.765}}

{“epoch”: 74, “max_epoch”: 100, “time_per_epoch”: “0:04:52.766306”, “eta”: “2:06:51.923960”, “date”: “3/18/2024”, “time”: “11:42:57”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 6.439, “face”: 9.0909}}, “graphical”: {“loss”: 0.0033511482179164886, “learning_rate”: “1.2983626e-05”}, “kpi”: {“validation cost”: 0.00139689, “mean average precision”: 7.765}}

{“epoch”: 75, “max_epoch”: 100, “time_per_epoch”: “0:04:53.221985”, “eta”: “2:02:10.549622”, “date”: “3/18/2024”, “time”: “11:47:51”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 6.439, “face”: 9.0909}}, “graphical”: {“loss”: 0.0034393512178212404, “learning_rate”: “1.2358605e-05”}, “kpi”: {“validation cost”: 0.00139689, “mean average precision”: 7.765}}

{“epoch”: 76, “max_epoch”: 100, “time_per_epoch”: “0:04:53.051231”, “eta”: “1:57:13.229536”, “date”: “3/18/2024”, “time”: “11:52:44”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 6.439, “face”: 9.0909}}, “graphical”: {“loss”: 0.0033900632988661528, “learning_rate”: “1.1763673e-05”}, “kpi”: {“validation cost”: 0.00139689, “mean average precision”: 7.765}}

{“epoch”: 77, “max_epoch”: 100, “time_per_epoch”: “0:04:53.020180”, “eta”: “1:52:19.464134”, “date”: “3/18/2024”, “time”: “11:57:37”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 6.439, “face”: 9.0909}}, “graphical”: {“loss”: 0.0035282729659229517, “learning_rate”: “1.1197368e-05”}, “kpi”: {“validation cost”: 0.00139689, “mean average precision”: 7.765}}

{“epoch”: 78, “max_epoch”: 100, “time_per_epoch”: “0:04:52.879410”, “eta”: “1:47:23.347021”, “date”: “3/18/2024”, “time”: “12:2:30”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 6.439, “face”: 9.0909}}, “graphical”: {“loss”: 0.0035071733873337507, “learning_rate”: “1.0658337e-05”}, “kpi”: {“validation cost”: 0.00139689, “mean average precision”: 7.765}}

{“epoch”: 79, “max_epoch”: 100, “time_per_epoch”: “0:04:53.092095”, “eta”: “1:42:34.934003”, “date”: “3/18/2024”, “time”: “12:7:23”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 6.439, “face”: 9.0909}}, “graphical”: {“loss”: 0.0034456606954336166, “learning_rate”: “1.0145253e-05”}, “kpi”: {“validation cost”: 0.00139689, “mean average precision”: 7.765}}

{“date”: “3/18/2024”, “time”: “12:13:47”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Evaluation metrics generated.”, “categorical”: {“average_precision”: {“person”: 10.7489, “face”: 0.0}}, “graphical”: {“loss”: 0.0034456606954336166, “learning_rate”: “1.0145253e-05”}, “kpi”: {“validation cost”: 0.00095147, “mean average precision”: 5.3745}}

{“epoch”: 80, “max_epoch”: 100, “time_per_epoch”: “0:06:24.593330”, “eta”: “2:08:11.866608”, “date”: “3/18/2024”, “time”: “12:13:47”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 10.7489, “face”: 0.0}}, “graphical”: {“loss”: 0.0036457465030252934, “learning_rate”: “9.65686e-06”}, “kpi”: {“validation cost”: 0.00095147, “mean average precision”: 5.3745}}

{“epoch”: 81, “max_epoch”: 100, “time_per_epoch”: “0:04:52.637888”, “eta”: “1:32:40.119880”, “date”: “3/18/2024”, “time”: “12:18:40”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 10.7489, “face”: 0.0}}, “graphical”: {“loss”: 0.0033868623431771994, “learning_rate”: “9.1919865e-06”}, “kpi”: {“validation cost”: 0.00095147, “mean average precision”: 5.3745}}

{“epoch”: 82, “max_epoch”: 100, “time_per_epoch”: “0:04:53.076957”, “eta”: “1:27:55.385217”, “date”: “3/18/2024”, “time”: “12:23:33”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 10.7489, “face”: 0.0}}, “graphical”: {“loss”: 0.0034721915144473314, “learning_rate”: “8.749492e-06”}, “kpi”: {“validation cost”: 0.00095147, “mean average precision”: 5.3745}}

{“epoch”: 83, “max_epoch”: 100, “time_per_epoch”: “0:04:53.120975”, “eta”: “1:23:03.056579”, “date”: “3/18/2024”, “time”: “12:28:26”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 10.7489, “face”: 0.0}}, “graphical”: {“loss”: 0.0034816497936844826, “learning_rate”: “8.328291e-06”}, “kpi”: {“validation cost”: 0.00095147, “mean average precision”: 5.3745}}

{“epoch”: 84, “max_epoch”: 100, “time_per_epoch”: “0:04:53.217334”, “eta”: “1:18:11.477337”, “date”: “3/18/2024”, “time”: “12:33:19”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 10.7489, “face”: 0.0}}, “graphical”: {“loss”: 0.00363638112321496, “learning_rate”: “7.927374e-06”}, “kpi”: {“validation cost”: 0.00095147, “mean average precision”: 5.3745}}

{“epoch”: 85, “max_epoch”: 100, “time_per_epoch”: “0:04:53.200366”, “eta”: “1:13:18.005483”, “date”: “3/18/2024”, “time”: “12:38:13”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 10.7489, “face”: 0.0}}, “graphical”: {“loss”: 0.00372359249740839, “learning_rate”: “7.545757e-06”}, “kpi”: {“validation cost”: 0.00095147, “mean average precision”: 5.3745}}

{“epoch”: 86, “max_epoch”: 100, “time_per_epoch”: “0:04:53.092091”, “eta”: “1:08:23.289268”, “date”: “3/18/2024”, “time”: “12:43:6”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 10.7489, “face”: 0.0}}, “graphical”: {“loss”: 0.0036191879771649837, “learning_rate”: “7.182504e-06”}, “kpi”: {“validation cost”: 0.00095147, “mean average precision”: 5.3745}}

{“epoch”: 87, “max_epoch”: 100, “time_per_epoch”: “0:04:53.017724”, “eta”: “1:03:29.230412”, “date”: “3/18/2024”, “time”: “12:47:59”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 10.7489, “face”: 0.0}}, “graphical”: {“loss”: 0.003481260035187006, “learning_rate”: “6.8367444e-06”}, “kpi”: {“validation cost”: 0.00095147, “mean average precision”: 5.3745}}

{“epoch”: 88, “max_epoch”: 100, “time_per_epoch”: “0:04:52.818674”, “eta”: “0:58:33.824083”, “date”: “3/18/2024”, “time”: “12:52:51”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 10.7489, “face”: 0.0}}, “graphical”: {“loss”: 0.0036475080996751785, “learning_rate”: “6.5076288e-06”}, “kpi”: {“validation cost”: 0.00095147, “mean average precision”: 5.3745}}

{“epoch”: 89, “max_epoch”: 100, “time_per_epoch”: “0:04:53.035219”, “eta”: “0:53:43.387406”, “date”: “3/18/2024”, “time”: “12:57:44”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 10.7489, “face”: 0.0}}, “graphical”: {“loss”: 0.0033387336879968643, “learning_rate”: “6.1943515e-06”}, “kpi”: {“validation cost”: 0.00095147, “mean average precision”: 5.3745}}

{“date”: “3/18/2024”, “time”: “13:3:24”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Evaluation metrics generated.”, “categorical”: {“average_precision”: {“person”: 37.2394, “face”: 9.0909}}, “graphical”: {“loss”: 0.0033387336879968643, “learning_rate”: “6.1943515e-06”}, “kpi”: {“validation cost”: 0.00038861, “mean average precision”: 23.1652}}

{“epoch”: 90, “max_epoch”: 100, “time_per_epoch”: “0:05:39.616845”, “eta”: “0:56:36.168447”, “date”: “3/18/2024”, “time”: “13:3:24”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 37.2394, “face”: 9.0909}}, “graphical”: {“loss”: 0.0034234789200127125, “learning_rate”: “5.89616e-06”}, “kpi”: {“validation cost”: 0.00038861, “mean average precision”: 23.1652}}

{“epoch”: 91, “max_epoch”: 100, “time_per_epoch”: “0:04:52.972059”, “eta”: “0:43:56.748533”, “date”: “3/18/2024”, “time”: “13:8:17”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 37.2394, “face”: 9.0909}}, “graphical”: {“loss”: 0.0034105521626770496, “learning_rate”: “5.6123245e-06”}, “kpi”: {“validation cost”: 0.00038861, “mean average precision”: 23.1652}}

{“epoch”: 92, “max_epoch”: 100, “time_per_epoch”: “0:04:53.128333”, “eta”: “0:39:05.026665”, “date”: “3/18/2024”, “time”: “13:13:10”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 37.2394, “face”: 9.0909}}, “graphical”: {“loss”: 0.0035817220341414213, “learning_rate”: “5.3421463e-06”}, “kpi”: {“validation cost”: 0.00038861, “mean average precision”: 23.1652}}

{“epoch”: 93, “max_epoch”: 100, “time_per_epoch”: “0:04:53.233027”, “eta”: “0:34:12.631186”, “date”: “3/18/2024”, “time”: “13:18:3”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 37.2394, “face”: 9.0909}}, “graphical”: {“loss”: 0.003499566577374935, “learning_rate”: “5.08498e-06”}, “kpi”: {“validation cost”: 0.00038861, “mean average precision”: 23.1652}}

{“epoch”: 94, “max_epoch”: 100, “time_per_epoch”: “0:04:53.174548”, “eta”: “0:29:19.047286”, “date”: “3/18/2024”, “time”: “13:22:57”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 37.2394, “face”: 9.0909}}, “graphical”: {“loss”: 0.003362390212714672, “learning_rate”: “4.8401935e-06”}, “kpi”: {“validation cost”: 0.00038861, “mean average precision”: 23.1652}}

{“epoch”: 95, “max_epoch”: 100, “time_per_epoch”: “0:04:53.259647”, “eta”: “0:24:26.298233”, “date”: “3/18/2024”, “time”: “13:27:50”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 37.2394, “face”: 9.0909}}, “graphical”: {“loss”: 0.0033713008742779493, “learning_rate”: “4.607186e-06”}, “kpi”: {“validation cost”: 0.00038861, “mean average precision”: 23.1652}}

{“epoch”: 96, “max_epoch”: 100, “time_per_epoch”: “0:04:53.144436”, “eta”: “0:19:32.577745”, “date”: “3/18/2024”, “time”: “13:32:43”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 37.2394, “face”: 9.0909}}, “graphical”: {“loss”: 0.0035697848070412874, “learning_rate”: “4.3854e-06”}, “kpi”: {“validation cost”: 0.00038861, “mean average precision”: 23.1652}}

{“epoch”: 97, “max_epoch”: 100, “time_per_epoch”: “0:04:53.071592”, “eta”: “0:14:39.214776”, “date”: “3/18/2024”, “time”: “13:37:36”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 37.2394, “face”: 9.0909}}, “graphical”: {“loss”: 0.0034839659929275513, “learning_rate”: “4.17429e-06”}, “kpi”: {“validation cost”: 0.00038861, “mean average precision”: 23.1652}}

{“epoch”: 98, “max_epoch”: 100, “time_per_epoch”: “0:04:52.816191”, “eta”: “0:09:45.632381”, “date”: “3/18/2024”, “time”: “13:42:29”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 37.2394, “face”: 9.0909}}, “graphical”: {“loss”: 0.003349154721945524, “learning_rate”: “3.9733436e-06”}, “kpi”: {“validation cost”: 0.00038861, “mean average precision”: 23.1652}}

{“epoch”: 99, “max_epoch”: 100, “time_per_epoch”: “0:04:52.947308”, “eta”: “0:04:52.947308”, “date”: “3/18/2024”, “time”: “13:47:22”, “status”: “RUNNING”, “verbosity”: “INFO”, “categorical”: {“average_precision”: {“person”: 37.2394, “face”: 9.0909}}, “graphical”: {“loss”: 0.0034041269682347775, “learning_rate”: “3.7820666e-06”}, “kpi”: {“validation cost”: 0.00038861, “mean average precision”: 23.1652}}

{“date”: “3/18/2024”, “time”: “13:52:51”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Evaluation metrics generated.”, “categorical”: {“average_precision”: {“person”: 47.8211, “face”: 6.8182}}, “graphical”: {“loss”: 0.0034041269682347775, “learning_rate”: “3.7820666e-06”}, “kpi”: {“validation cost”: 0.00032869, “mean average precision”: 27.3196}}

{“date”: “3/18/2024”, “time”: “13:52:51”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Training loop completed.”, “categorical”: {“average_precision”: {“person”: 47.8211, “face”: 6.8182}}, “graphical”: {“loss”: 0.0034041269682347775, “learning_rate”: “3.7820666e-06”}, “kpi”: {“validation cost”: 0.00032869, “mean average precision”: 27.3196}}

{“date”: “3/18/2024”, “time”: “13:52:51”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Saving trained model.”, “categorical”: {“average_precision”: {“person”: 47.8211, “face”: 6.8182}}, “graphical”: {“loss”: 0.0034041269682347775, “learning_rate”: “3.7820666e-06”}, “kpi”: {“validation cost”: 0.00032869, “mean average precision”: 27.3196}}

{“date”: “3/18/2024”, “time”: “13:52:51”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Model saved.”, “categorical”: {“average_precision”: {“person”: 47.8211, “face”: 6.8182}}, “graphical”: {“loss”: 0.0034041269682347775, “learning_rate”: “3.7820666e-06”}, “kpi”: {“size”: 81.67167663574219, “param_count”: 21.319754}}

{“date”: “3/18/2024”, “time”: “13:52:51”, “status”: “RUNNING”, “verbosity”: “INFO”, “message”: “Training op complete.”, “categorical”: {“average_precision”: {“person”: 47.8211, “face”: 6.8182}}, “graphical”: {“loss”: 0.0034041269682347775, “learning_rate”: “3.7820666e-06”}, “kpi”: {“size”: 81.67167663574219, “param_count”: 21.319754}}

{“date”: “3/18/2024”, “time”: “13:52:51”, “status”: “SUCCESS”, “verbosity”: “INFO”, “message”: “DetectNet_v2 training job complete.”, “categorical”: {“average_precision”: {“person”: 47.8211, “face”: 6.8182}}, “graphical”: {“loss”: 0.0034041269682347775, “learning_rate”: “3.7820666e-06”}, “kpi”: {“size”: 81.67167663574219, “param_count”: 21.319754}}

1 Like

AutoML is a TAO Toolkit API service that automatically selects deep learning hyperparameters for a chosen model and dataset.
It makes sense that autoML result can get a better mAP.

BTW, you can upload .txt file via
image as an attachment.

Yes
I used AutoML in TAO Toolkit API Service to get the best model by running for 15 experiments and 25 epochs which gave the experiment_spec_best.txt as the best possible config.
experiment_spec_best.txt (4.7 KB)

Post that I used the best experiment_spec for training without AutoML, but that gave me worse results.
The following is the status.json file for training for 100 epochs without automl.
status.txt (56.5 KB)

Although, the experiment spec obtained from automl was used, the results doesn’t look as good when not using automl in tao toolkit api.

Could you please use the exact training spec file and run again without automl? Please use the same num_epochs as well.

Okay
Let me try that out.