RuVic / RICE detection Public

Experiments (2 / 10)

	Name	Input	DSP Blocks	Learn Blocks	f32_V_Acc	f32_T_Acc	i8_V_Acc	i8_T_Acc	f32_Latency	f32_Flash	i8_Latency	i8_Flash
	Image data, Image, Object Detection (Images) #2 (original)	96×96	Image	FOMO MobileNetV2 0.35	100.0%	-	99.0%	-	4 ms.	112.6K	4 ms.	90.0K	View impulse
	Image data, Image, Object Detection (Images) #1 (2 convd)	96×96	Image	Rice detection	-	-	-	-	-	-	-	-	View impulse

Select which columns to show in the impulse list:

Source - Impulse source (Manual vs EON Tuner)

Created by - The user who originally created this impulse

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_Flash - float32 Flash for the complete impulse.

i8_Latency - int8 Latency for the complete impulse.

i8_Flash - int8 Flash for the complete impulse.

Img_Height - Image height

Img_Width - Image width

Resize_Mode - Resize mode

Norm - Normalization

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

Model - ML Model architecture (Learn Block #1)

Tc - Training cycles (Learn Block #1)

Lr - Learning rate (Learn Block #1)

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

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

Data_Aug - Data augmentation (Learn Block #1)

Val_Size - Validation set size (Learn Block #1)

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

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

f32_V_Precision - float32 Precision (non-background) (validation set)

f32_V_Recall - float32 Recall (non-background) (validation set)

f32_V_F1 - float32 F1 Score (non-background) (validation set)

i8_V_Precision - int8 Precision (non-background) (validation set)

i8_V_Recall - int8 Recall (non-background) (validation set)

i8_V_F1 - int8 F1 Score (non-background) (validation set)

Configure your target device for model performance calculations, and to enable target specific optimizations.

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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.

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| KB

Max

ROM

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Latency

| ms

Max

YEARLY

$400 /month

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SAVE 15%

MONTHLY

$475 /month

billed monthly

Experiments (2 / 10)

Image data, Image, Object Detection (Images) #2 (original)

Image data, Image, Object Detection (Images) #1 (2 convd)