Advanced vehicle security monitoring - Impulses

Experiments

Name	Input	DSP Blocks	Learn Blocks	f32_V_Acc	f32_T_Acc	i8_V_Acc	i8_T_Acc	f32_Latency	f32_RAM	f32_Flash	i8_Latency	i8_RAM	i8_Flash
Impulse #1	128×128	Image	Transfer learning	100.0%	100.0%	100.0%	-	1001 ms.	482.5K	8.8M	322 ms.	735.5K	2.6M	View impulse
Impulse #2	128×128	Image	Transfer learning	100.0%	97.3%	100.0%	-	365 ms.	763.4K	1.8M	187 ms.	313.1K	637.0K	View impulse
Impulse #3	128×128	Image	Transfer learning, Visual Anomaly Detection	100.0%	97.3%	100.0%	-	394 ms.	857.3K	1.9M	212 ms.	318.1K	768.2K	View impulse

Impulse information

Tags - Impulse tags

Source - Impulse source (Manual vs EON Tuner)

Created by - The user who originally created this impulse

Key metrics

f32_V_Acc - Accuracy (float32, validation set)

f32_T_Acc - Accuracy (float32, test set)

i8_V_Acc - Accuracy (int8, validation set)

i8_T_Acc - Accuracy (int8, test set)

f32_Latency - float32 Latency for the complete impulse.

f32_RAM - float32 RAM for the complete impulse.

f32_Flash - float32 Flash for the complete impulse.

i8_Latency - int8 Latency for the complete impulse.

i8_RAM - int8 RAM for the complete impulse.

i8_Flash - int8 Flash for the complete impulse.

Input block config

Img_Height - Image height

Img_Width - Image width

Resize_Mode - Resize mode

Processing block config

Col_dep - Image > Color depth (Processing Block #1)

Learn block config

L_1_Model - ML Model architecture (Learn Block #1)

L_1_Notc - Number of training cycles (Learn Block #1)

L_1_Mlr - Max learning rate (Learn Block #1)

L_1_Pre_bac - Pretrained backbone (Learn Block #1)

L_1_Arc - Architecture (Learn Block #1)

L_1_Alp - Alpha (Learn Block #1)

L_1_Alp_ALB1 - Alpha (Learn Block #1)

L_1_Bat_siz - Batch size (Learn Block #1)

L_1_Ran_cro - Random crop (Learn Block #1)

L_1_Cen_cro - Center crop (Learn Block #1)

L_1_Col_aug - Color augmentation (Learn Block #1)

L_1_Hor_fli - Horizontal flip (Learn Block #1)

L_1_Lab_smo - Label smoothing (Learn Block #1)

L_1_Mix_aug - Mixup augmentation (Learn Block #1)

L_1_Fre_blo - Freeze blocks (Learn Block #1)

L_1_Fre_blo_FbLB1 - Freeze blocks (Learn Block #1)

L_1_Val_Size - Validation set size (Learn Block #1)

L_1_Val_Key - Split train/validation set on metadata key (Learn Block #1)

L_1_Mode - Visual / expert mode (Learn Block #1)

L_1_Tc - Training cycles (Learn Block #1)

L_1_Lr - Learning rate (Learn Block #1)

L_1_Lo - Use learned optimizer? (Learn Block #1)

L_1_Do - Dropout (Learn Block #1)

L_1_Ne - Neurons (Learn Block #1)

L_1_Acw - Auto-weight classes (Learn Block #1)

L_1_Data_Aug - Data augmentation (Learn Block #1)

L_2_Model - ML Model architecture (Learn Block #2)

L_2_Anom_Cap - Anomaly capacity (Learn Block #2)

L_2_Backbone - Backbone (Learn Block #2)

L_2_Val_Size - Validation set size (Learn Block #2)

L_2_Val_Key - Split train/validation set on metadata key (Learn Block #2)

L_2_Mode - Visual / expert mode (Learn Block #2)

Accuracy metrics

f32_V_AUC - float32 Area under ROC Curve (validation set)

f32_V_WA_P - float32 Weighted average Precision (validation set)

f32_V_WA_R - float32 Weighted average Recall (validation set)

f32_V_WA_F1 - float32 Weighted average F1 score (validation set)

i8_V_AUC - int8 Area under ROC Curve (validation set)

i8_V_WA_P - int8 Weighted average Precision (validation set)

i8_V_WA_R - int8 Weighted average Recall (validation set)

i8_V_WA_F1 - int8 Weighted average F1 score (validation set)

Target device

Define your target device requirements to inform model optimizations and performance calculations. No device yet? Use the default settings which you can change at any time.

Target device

Processor family

Clock rate

| MHz

Max

Custom device name

Application budget

Specify the available RAM and ROM for the model's operation, along with the maximum allowed latency for your specific application. Not sure yet? Start with the defaults and modify them later on.

RAM

| KB

Max

ROM

| KB

Max

Latency

| ms

Max

Experiments

Impulse #1

Impulse #2

Impulse #3