Image Classification project using BrainChip MetaTF and Akidanet models - Impulses

Experiments

	Name	Input	DSP Blocks	Learn Blocks	f32_V_Acc	f32_T_Acc	i8_V_Acc	i8_T_Acc	akida_V_Acc	akida_T_Acc	f32_Latency	f32_Flash	i8_Latency	i8_Flash	akida_Latency	akida_Flash
	Image data #1 (EON Tuner Primary), Image, Transfer Learning (Images) - BrainChip Akida™ #1	160×160	Image	AkidaNet (alpha=0.50) @160x160x3	100.0%	100.0%	100.0%	-	97.8%	-	20119 ms.	3.4M	2 ms.	696.9K	2 ms.	696.9K	View impulse
	Image data #1 (EON Tuner Primary), Image, Transfer Learning (Images) - BrainChip Akida™ #2 (Clone of version #1)	160×160	Image	Transfer learning	-	-	-	-	-	-	-	-	-	-	-	-	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)

akida_V_Acc - Accuracy (akida, validation set)

akida_T_Acc - Accuracy (akida, 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.

akida_Latency - akida Latency for the complete impulse.

akida_Flash - akida 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

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)

Do - Dropout (Learn Block #1)

Ne - Neurons (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)

Accuracy metrics

No columns

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

Image data #1 (EON Tuner Primary), Image, Transfer Learning (Images) - BrainChip Akida™ #1

Image data #1 (EON Tuner Primary), Image, Transfer Learning (Images) - BrainChip Akida™ #2 (Clone of version #1)