Brainchip-Pneumonia - Impulses

Experiments

Name	Input	DSP Blocks	Learn Blocks	f32_V_Acc	f32_T_Acc	akida_V_Acc	akida_T_Acc	f32_Latency	akida_Latency
Image data, Image, Classification - BrainChip Akida™ #1 (40e 5v)	640×480	Image	Classifier	96.8%	94.1%	95.9%	-			View impulse

Impulse information

Tags - Impulse tags

Source - Impulse source (Manual vs EON Tuner)

Key metrics

f32_V_Acc - Accuracy (float32, validation set)

f32_T_Acc - Accuracy (float32, test set)

akida_V_Acc - Accuracy (akida, validation set)

akida_T_Acc - Accuracy (akida, test set)

f32_Latency - float32 Latency for the complete impulse.

akida_Latency - akida Latency 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

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)

Accuracy metrics

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, Image, Classification - BrainChip Akida™ #1 (40e 5v)