pet_detection - Impulses

Experiments

Name	Input	DSP Blocks	Learn Blocks	Tr_Proc	f32_Latency	f32_Flash	i8_Latency	i8_Flash	f32_V_mAP@[IoU=50]	f32_T_mAP@[IoU=50]	i8_V_mAP@[IoU=50]	i8_T_mAP@[IoU=50]
Cat and Dog Detector	320×320	Image	Object detection	GPU	33 ms.	13.5M	4 ms.	7.0M	0.99	0.98	0.99	0.97	View impulse
can and dog - nano	160×160	Image	YOLOv5	GPU	1 ms.	-	1 ms.	-	0.95	0.89	0.94	0.89	View impulse
Cat and Dog Detector YoloPro	320×320	Image	FOMO MobileNetV2 0.35	GPU	4 ms.	82.9K	2 ms.	55.3K	-	-	-	-	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)

f32_T_mAP@[IoU=50] - Mean Average Precision (mAP) [IoU=50] (float32, test set)

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

i8_T_mAP@[IoU=50] - Mean Average Precision (mAP) [IoU=50] (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

Tc - Training cycles (Learn Block #1)

Lr - Learning rate (Learn Block #1)

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

Bs - Batch size (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_St - Score threshold (Learn Block #1)

Model - ML Model architecture (Learn Block #1)

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

Mod_siz - Model size (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_T_mAP - float32 mAP (test set)

f32_T_mAP@[IoU=75] - float32 mAP@[IoU=75] (test set)

f32_T_mAP@[area=s] - float32 mAP@[area=small] (test set)

f32_T_mAP@[area=m] - float32 mAP@[area=medium] (test set)

f32_T_mAP@[area=l] - float32 mAP@[area=large] (test set)

f32_T_Recall@[max_det=1] - float32 Recall@[max_detections=1] (test set)

f32_T_Recall@[max_det=10] - float32 Recall@[max_detections=10] (test set)

f32_T_Recall@[max_det=100] - float32 Recall@[max_detections=100] (test set)

f32_T_Recall@[area=s] - float32 Recall@[area=small] (test set)

f32_T_Recall@[area=m] - float32 Recall@[area=medium] (test set)

f32_T_Recall@[area=l] - float32 Recall@[area=large] (test 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_T_mAP - int8 mAP (test set)

i8_T_mAP@[IoU=75] - int8 mAP@[IoU=75] (test set)

i8_T_mAP@[area=s] - int8 mAP@[area=small] (test set)

i8_T_mAP@[area=m] - int8 mAP@[area=medium] (test set)

i8_T_mAP@[area=l] - int8 mAP@[area=large] (test set)

i8_T_Recall@[max_det=1] - int8 Recall@[max_detections=1] (test set)

i8_T_Recall@[max_det=10] - int8 Recall@[max_detections=10] (test set)

i8_T_Recall@[max_det=100] - int8 Recall@[max_detections=100] (test set)

i8_T_Recall@[area=s] - int8 Recall@[area=small] (test set)

i8_T_Recall@[area=m] - int8 Recall@[area=medium] (test set)

i8_T_Recall@[area=l] - int8 Recall@[area=large] (test set)

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

f32_T_Acc - Legacy "Accuracy" metric (float32, test set)

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

i8_T_Acc - Legacy "Accuracy" metric (int8, test 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

Cat and Dog Detector

can and dog - nano

Cat and Dog Detector YoloPro