Car Detection v2 - Impulses

Experiments (6 / 10)

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
CellPhone 160x160 Color 0.35 V2	160×160	Image (RGB)	MobileNetV2 160x160 0.35, MobileNetV2 0.1 + GMM	98.7%	98.6%	98.6%	98.6%	636 ms.	1.2M	2.7M	326 ms.	453.3K	997.9K	View impulse
Pico 96x96 Greyscale 0.1 V1	96×96	Image (Grayscale)	MobileNetV1 96x96 0.1	91.3%	84.4%	84.7%	76.2%	63 ms.	106.9K	191.8K	54 ms.	60.1K	107.6K	View impulse
Nicla 96x96 V1 and V2 Anomaly 0.1	96×96	Image (RGB)	MobileNetV1 96x96 0.1, MobileNetV2 0.1 + GMM	92.2%	85.1%	82.1%	75.3%	89 ms.	429.3K	301.7K	70 ms.	153.2K	211.7K	View impulse
H7 Portenta 96x96 Color	96×96	Image (RGB)	MobileNetV2 96x96 0.1, MobileNetV2 0.1 + GMM	97.1%	95.4%	96.3%	94.6%	150 ms.	450.1K	568.2K	99 ms.	173.9K	348.5K	View impulse
H7 Portenta 96x96 Greyscale	96×96	Image (Grayscale)	MobileNetV2 96x96 0.1, MobileNetV2 0.1 + GMM	96.8%	95.1%	96.5%	94.5%	165 ms.	450.1K	884.7K	97 ms.	173.9K	427.7K	View impulse
Pico 96x96 Greyscale 0.2 V1	96×96	Image (Grayscale)	MobileNetV1 96x96 0.2	94.7%	91.6%	86.0%	81.6%	115 ms.	164.4K	581.9K	68 ms.	95.6K	217.8K	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

Norm - Normalization

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

Learn block config

L_1_Model - ML Model architecture (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_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_Thr_Mcr - Minimum confidence rating (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)

L_2_Thr_Msbtaa - Minimum score before tagging as anomaly (Learn Block #2)

L_2_Thr_Mtcas - Method to calculate anomaly score (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)

f32_T_AUC - float32 Area under ROC Curve (test set)

f32_T_WA_P - float32 Weighted average Precision (test set)

f32_T_WA_R - float32 Weighted average Recall (test set)

f32_T_WA_F1 - float32 Weighted average F1 score (test 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)

i8_T_AUC - int8 Area under ROC Curve (test set)

i8_T_WA_P - int8 Weighted average Precision (test set)

i8_T_WA_R - int8 Weighted average Recall (test set)

i8_T_WA_F1 - int8 Weighted average F1 score (test set)

Post-processing block config

No columns

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 (6 / 10)

CellPhone 160x160 Color 0.35 V2

Pico 96x96 Greyscale 0.1 V1

Nicla 96x96 V1 and V2 Anomaly 0.1

H7 Portenta 96x96 Color

H7 Portenta 96x96 Greyscale

Pico 96x96 Greyscale 0.2 V1