Edge Impulse Inc. / Car Parking Occupancy Detection - FOMO Public
Target: Arduino Portenta H7 (Cortex-M7 480MHz) Clone this project
Experiments EON Tuner
The EON Tuner helps you quickly run hyper-parameter sweeps that explore different pre-processing + model architectures optimized for your defined objectives. Clone this project to use the EON Tuner.

Target

Run #1

Arduino Portenta H7 (Cortex-M7 480MHz)

100 ms

440 kB

2048 kB

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General

F1-score

Precision

Recall

96%
rgb-fomo-ab4
PERFORMANCE
LATENCY
190 ms of 100 ms
Exceeds target by 90 ms
RAM
627 kB of 440 kB
Exceeds target by 187 kB
ROM
59 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

160 |
160

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.01 | 60 | 96%

FOMO (MobileNetV2 0.1) | int8

1 6/5/2025, 8:55:03 AM

92%
grayscale-fomo-8c0
PERFORMANCE
LATENCY
60 ms of 100 ms
RAM
240 kB of 440 kB
ROM
59 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

Grayscale

OBJECT DETECTION (IMAGES)

0.1 | 39 | 92%

FOMO (MobileNetV2 0.1) | int8

0 6/5/2025, 8:37:45 AM

92%
grayscale-fomo-34b
PERFORMANCE
LATENCY
196 ms of 100 ms
Exceeds target by 96 ms
RAM
630 kB of 440 kB
Exceeds target by 190 kB
ROM
72 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

160 |
160

IMAGE

Grayscale

OBJECT DETECTION (IMAGES)

0.1 | 60 | 92%

FOMO (MobileNetV2 0.35) | int8

1 6/5/2025, 8:58:33 AM

92%
rgb-fomo-b6f
PERFORMANCE
LATENCY
181 ms of 100 ms
Exceeds target by 81 ms
RAM
627 kB of 440 kB
Exceeds target by 187 kB
ROM
59 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

160 |
160

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.01 | 30 | 92%

FOMO (MobileNetV2 0.1) | int8

1 6/5/2025, 8:57:16 AM

92%
grayscale-fomo-a1f
PERFORMANCE
LATENCY
66 ms of 100 ms
RAM
240 kB of 440 kB
ROM
59 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

Grayscale

OBJECT DETECTION (IMAGES)

0.1 | 31 | 92%

FOMO (MobileNetV2 0.1) | int8

2 6/5/2025, 9:07:31 AM

92%
grayscale-fomo-214
PERFORMANCE
LATENCY
68 ms of 100 ms
RAM
240 kB of 440 kB
ROM
59 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

Grayscale

OBJECT DETECTION (IMAGES)

0.1 | 45 | 92%

FOMO (MobileNetV2 0.1) | int8

2 6/5/2025, 9:07:10 AM

88%
rgb-fomo-167
PERFORMANCE
LATENCY
54 ms of 100 ms
RAM
243 kB of 440 kB
ROM
72 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.1 | 45 | 88%

FOMO (MobileNetV2 0.35) | int8

0 6/5/2025, 8:37:20 AM

88%
rgb-fomo-38c
PERFORMANCE
LATENCY
73 ms of 100 ms
RAM
243 kB of 440 kB
ROM
72 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.01 | 37 | 88%

FOMO (MobileNetV2 0.35) | int8

0 6/5/2025, 8:45:44 AM

88%
rgb-fomo-25c
PERFORMANCE
LATENCY
88 ms of 100 ms
RAM
243 kB of 440 kB
ROM
72 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.1 | 34 | 88%

FOMO (MobileNetV2 0.35) | int8

2 6/5/2025, 9:08:01 AM

88%
rgb-fomo-337
PERFORMANCE
LATENCY
88 ms of 100 ms
RAM
243 kB of 440 kB
ROM
72 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.01 | 55 | 88%

FOMO (MobileNetV2 0.35) | int8

3 6/5/2025, 9:18:06 AM

88%
rgb-fomo-fcc
PERFORMANCE
LATENCY
80 ms of 100 ms
RAM
243 kB of 440 kB
ROM
72 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.1 | 56 | 88%

FOMO (MobileNetV2 0.35) | int8

3 6/5/2025, 9:18:08 AM

85%
grayscale-fomo-201
PERFORMANCE
LATENCY
87 ms of 100 ms
RAM
243 kB of 440 kB
ROM
72 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

Grayscale

OBJECT DETECTION (IMAGES)

0.01 | 39 | 85%

FOMO (MobileNetV2 0.35) | int8

0 6/5/2025, 8:33:18 AM

85%
rgb-fomo-cc6
PERFORMANCE
LATENCY
59 ms of 100 ms
RAM
243 kB of 440 kB
ROM
72 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.01 | 57 | 85%

FOMO (MobileNetV2 0.35) | int8

0 6/5/2025, 8:38:41 AM

85%
rgb-fomo-0dd
PERFORMANCE
LATENCY
73 ms of 100 ms
RAM
243 kB of 440 kB
ROM
72 kB of 2048 kB
DSP NN Unused
IMAGE INPUT

96 |
96

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.01 | 47 | 85%

FOMO (MobileNetV2 0.35) | int8

3 6/5/2025, 9:20:10 AM

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