Rock Paper Scissor Game - Impulses

Experiments

Name	Input	DSP Blocks	Learn Blocks	Tr_Proc	f32_Latency	f32_Flash	i8_Latency	i8_Flash	f32_V_mAP@[IoU=50]	i8_V_mAP@[IoU=50]	f32_T_Acc	i8_T_Acc
Impulse YOLO-Pro	320×320	Image	YOLO-Pro	GPU	1 ms.	-	1 ms.	-	0.91	0.89	-	-	View impulse
Impulse FOMO	96×96	Image	FOMO MobileNetV2 0.35	CPU	1 ms.	-	1 ms.	-	-	-	-	-	View impulse
Impulse YOLO-Pro (fit shortest crop)	320×320	Image	YOLO-Pro	GPU	1 ms.	-	1 ms.	-	0.90	0.90	-	-	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

Tr_Proc - Training processor

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.

f32_V_mAP@[IoU=50] - Mean Average Precision (mAP) [IoU=50] (float32, validation set)

i8_V_mAP@[IoU=50] - Mean Average Precision (mAP) [IoU=50] (int8, validation set)

f32_T_Acc - Accuracy (float32, test set)

i8_T_Acc - Accuracy (int8, test set)

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)

Notc - Number of training cycles (Learn Block #1)

Lea_rat - Learning rate (Learn Block #1)

Mod_siz - Model size (Learn Block #1)

Upw - Use pretrained weights (Learn Block #1)

Bat_siz - Batch size (Learn Block #1)

Fre_bac - Freeze backbone (Learn Block #1)

Arc_typ - Architecture type (Learn Block #1)

Spa_Aug - Spatial Augmentation (Learn Block #1)

CsA - Color space Augmentation (Learn Block #1)

Esse - Early stopping start epoch (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_St - Score threshold (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)

Accuracy metrics

f32_V_mAP - float32 mAP (validation set)

f32_V_mAP@[IoU=75] - float32 mAP@[IoU=75] (validation set)

f32_V_mAP@[area=s] - float32 mAP@[area=small] (validation set)

f32_V_mAP@[area=m] - float32 mAP@[area=medium] (validation set)

f32_V_mAP@[area=l] - float32 mAP@[area=large] (validation set)

f32_V_Recall@[max_det=1] - float32 Recall@[max_detections=1] (validation set)

f32_V_Recall@[max_det=10] - float32 Recall@[max_detections=10] (validation set)

f32_V_Recall@[max_det=100] - float32 Recall@[max_detections=100] (validation set)

f32_V_Recall@[area=s] - float32 Recall@[area=small] (validation set)

f32_V_Recall@[area=m] - float32 Recall@[area=medium] (validation set)

f32_V_Recall@[area=l] - float32 Recall@[area=large] (validation set)

f32_V_Precision score (legacy) - float32 Precision score (legacy) (validation set)

i8_V_mAP - int8 mAP (validation set)

i8_V_mAP@[IoU=75] - int8 mAP@[IoU=75] (validation set)

i8_V_mAP@[area=s] - int8 mAP@[area=small] (validation set)

i8_V_mAP@[area=m] - int8 mAP@[area=medium] (validation set)

i8_V_mAP@[area=l] - int8 mAP@[area=large] (validation set)

i8_V_Recall@[max_det=1] - int8 Recall@[max_detections=1] (validation set)

i8_V_Recall@[max_det=10] - int8 Recall@[max_detections=10] (validation set)

i8_V_Recall@[max_det=100] - int8 Recall@[max_detections=100] (validation set)

i8_V_Recall@[area=s] - int8 Recall@[area=small] (validation set)

i8_V_Recall@[area=m] - int8 Recall@[area=medium] (validation set)

i8_V_Recall@[area=l] - int8 Recall@[area=large] (validation set)

i8_V_Precision score (legacy) - int8 Precision score (legacy) (validation set)

f32_V_Acc - Legacy "Accuracy" metric (float32, validation set)

i8_V_Acc - Legacy "Accuracy" metric (int8, validation set)

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)

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

Impulse YOLO-Pro

Impulse FOMO

Impulse YOLO-Pro (fit shortest crop)