Latte Panda IOTA Punch Card - Impulses

	Name	Model filename	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	-	96×96	Image	Classifier	100.0%	-	100.0%	-	53 ms.	721.6K	544.4K	30 ms.	182.7K	160.1K	View impulse
	Impulse #2	Pretrained model				-	-	-	-	-	-	-	-	-	-	View impulse

Tags - Impulse tags

Source - Impulse source (Manual vs EON Tuner)

Model filename - Pretrained model filename

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_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.

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)

Thr_Mcr - Minimum confidence rating (Learn Block #1)

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_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)

No columns