Dashboard
Devices
Data acquisition
Experiments
- EON Tuner
Impulse design
Impulse #1
Versioning

Getting started

Documentation
Forums

Edge Impulse Experts / FOMO-AD Fabric Public

Clone this project

Edge Impulse Experts / FOMO-AD Fabric Public

Target: Raspberry Pi 4 Clone this project

Experiments EON Tuner

The EON Tuner helps you find the most optimal architecture for your embedded machine-learning application. Clone this project to use the EON Tuner.

Target

Raspberry Pi

Raspberry Pi 4

100 ms

8388608 kB

33554432 kB

Filters

Status

Pending 0

Running 0

Completed 30

Failed 0

DSP type

RGB 30

Model type

FOMO-AD 30

View

Data set

Validation

Test

Variant

Sort

General

Accuracy

Latency

RAM

ROM

Last updated

F1-score

Precision

Recall

Run #3 - Raspberry Pi (May 10 2024, 12:04:04, finished)

Run #3 - Raspberry Pi (May 10 2024, 12:04:04, finished) Run #2 (Apr 23 2024, 14:30:15, finished) Run #1 (Apr 23 2024, 13:32:39, finished)

82%

rgb-fomo-ad-52b

PERFORMANCE

LATENCY

22 ms of 100 ms

RAM

1205 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 11.1

5/10/2024, 12:24:29 PM

82%

rgb-fomo-ad-bae

PERFORMANCE

LATENCY

20 ms of 100 ms

RAM

1205 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 11.1

5/10/2024, 12:19:50 PM

82%

rgb-fomo-ad-171

PERFORMANCE

LATENCY

25 ms of 100 ms

RAM

1205 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 5.1

5/10/2024, 12:15:18 PM

82%

rgb-fomo-ad-7cb

PERFORMANCE

LATENCY

30 ms of 100 ms

RAM

1205 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 5.1

5/10/2024, 12:14:50 PM

82%

rgb-fomo-ad-2da

PERFORMANCE

LATENCY

23 ms of 100 ms

RAM

1205 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 7.1

5/10/2024, 12:10:20 PM

82%

rgb-fomo-ad-c33

PERFORMANCE

LATENCY

29 ms of 100 ms

RAM

1205 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 7.1

5/10/2024, 12:09:23 PM

81%

rgb-fomo-ad-467

PERFORMANCE

LATENCY

22 ms of 100 ms

RAM

1205 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 11.1

5/10/2024, 12:19:44 PM

79%

rgb-fomo-ad-85f

PERFORMANCE

LATENCY

14 ms of 100 ms

RAM

626 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

160 |

160

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 7.1

5/10/2024, 12:29:24 PM

79%

rgb-fomo-ad-ac7

PERFORMANCE

LATENCY

10 ms of 100 ms

RAM

626 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

160 |

160

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 5.1

5/10/2024, 12:15:30 PM

77%

rgb-fomo-ad-c37

PERFORMANCE

LATENCY

12 ms of 100 ms

RAM

626 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

160 |

160

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 7.1

5/10/2024, 12:28:50 PM

77%

rgb-fomo-ad-394

PERFORMANCE

LATENCY

15 ms of 100 ms

RAM

626 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

160 |

160

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 5.1

5/10/2024, 12:10:35 PM

77%

rgb-fomo-ad-7d2

PERFORMANCE

LATENCY

12 ms of 100 ms

RAM

626 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

160 |

160

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 11.1

5/10/2024, 12:10:24 PM

74%

rgb-fomo-ad-261

PERFORMANCE

LATENCY

9 ms of 100 ms

RAM

630 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

160 |

160

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 7.1

5/10/2024, 12:24:42 PM

74%

rgb-fomo-ad-f93

PERFORMANCE

LATENCY

17 ms of 100 ms

RAM

630 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

160 |

160

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 11.1

5/10/2024, 12:10:26 PM

73%

rgb-fomo-ad-c2d

PERFORMANCE

LATENCY

5 ms of 100 ms

RAM

239 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

96 |

96

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 5.1

5/10/2024, 12:14:12 PM

Training output

Hide

Click 'Run EON Tuner' to begin

EON Tuner settings

Find the optimal architecture for your machine learning model

The EON™ Tuner will evaluate many candidate model architectures (selected based on your target device and latency requirements) concurrently to help you find the best performing architecture for your application.

The search process can take up to 6 hours to complete. While the EON Tuner is running you can view the progress on this page at any time.

Name

Search space configuration

USE CASE

Continuous motion | Classification Continuous motion | Classification & anomaly Motion events | Classification

TUNER CONFIGURATION

Raspberry Pi (May 10 2024), finished Run #2 (Apr 23 2024), finished Run #1 (Apr 23 2024), finished

IMPULSE CONFIGURATION

Impulse #1

INPUT

Time-series
Pre-processed features

DSP

Raw
Spectral analysis
IMU (Syntiant)

LEARN

Dense
Convolutional (1D)

ANOMALY

Anomaly (k-means)
Anomaly (GMM)

ORGANIZATION (DSP)

Add custom DSP block

ORGANIZATION (ML)

Add custom ML block

Custom search space

Do more with EON Tuner

Access more parallel EON Tuner jobs for faster results, a fully customizable search space, and more model variant options.

© 2024 EdgeImpulse Inc. All rights reserved

Show logs

Waiting...

Cancel job

Help & documentation Get help on the forum Send feedback Contact sales

Join us

Terms of Service Privacy policy

Send us your feedback!

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

Subject

This field is required

How was your experience?

This field is required

Almost there!

You'll need a free Edge Impulse account to clone this project. Creating an account lets you add your own data, modify models, and join a community of thousands of embedded machine learning developers!

Sign up for free

I already have an account

Configure your target device and application budget

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 type

Processor family

Processor architecture

Clock rate

| MHz

Max

Accelerator

Device ID (optional)

Custom device name (optional)

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

Choose your pricing

YEARLY

$400 /month

billed annually

SAVE 15%

MONTHLY

$475 /month

billed monthly

Additional usage

Professional Plan includes 1,000 compute minutes per month. Additional usage will be charged at $0.10 per minute, billed monthly.

Upgrade to Enterprise

Why is this the right time for your team to invest in edge AI? What best describes your use case? What is your expected timeline for solving your problem?