Dan / Dog Doorbell Public

Experiments (2 / 10)

	Name	Input	DSP Blocks	Learn Blocks	f32_V_Acc	f32_T_Acc	f32_Latency	f32_Flash
	Impulse ModelNet 320	320×320	Image 320	MobileNetV2 SSD FPN-Lite	80.1%	75.0%	88 ms.	11.0M	View impulse
	Impulse #3	320×320	Image	MobileNetV2 SSD FPN-Lite	78.0%	78.6%	89 ms.	11.0M	View impulse

Select which columns to show in the impulse list:

Source - Impulse source (Manual vs EON Tuner)

Created by - The user who originally created this impulse

f32_V_Acc - Accuracy (float32, validation set)

f32_T_Acc - Accuracy (float32, test set)

f32_Latency - float32 Latency for the complete impulse.

f32_Flash - float32 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_St - Score threshold (Learn Block #1)

f32_V_mAP - float32 mAP (validation set)

f32_V_mAP@[IoU=50] - float32 mAP@[IoU=50] (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=50] - float32 mAP@[IoU=50] (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)

Configure your target device for model performance calculations, and to enable target specific optimizations.

We're always looking for ways to improve Edge Impulse. If you have any feedback, please let us know!

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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

YEARLY

$400 /month

billed annually

SAVE 15%

MONTHLY

$475 /month

billed monthly

Experiments (2 / 10)

Impulse ModelNet 320

Impulse #3