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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

Run #2 (), finished)

Run #3 - Raspberry Pi (), finished) Run #2 (), finished) Run #1 (), finished)

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

No name set

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

90%

rgb-fomo-ad-8e1

PERFORMANCE

LATENCY

47 ms of 100 ms

RAM

2433 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 7.1

4/23/2024, 2:52:41 PM

90%

rgb-fomo-ad-c8a

PERFORMANCE

LATENCY

48 ms of 100 ms

RAM

2433 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 11.1

4/23/2024, 2:47:35 PM

90%

rgb-fomo-ad-c69

PERFORMANCE

LATENCY

34 ms of 100 ms

RAM

1209 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 11.1

4/23/2024, 2:39:12 PM

87%

rgb-fomo-ad-c95

PERFORMANCE

LATENCY

62 ms of 100 ms

RAM

2437 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 7.1

4/23/2024, 2:48:58 PM

87%

rgb-fomo-ad-14e

PERFORMANCE

LATENCY

79 ms of 100 ms

RAM

2437 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 5.1

4/23/2024, 2:38:09 PM

84%

rgb-fomo-ad-6e8

PERFORMANCE

LATENCY

46 ms of 100 ms

RAM

2433 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 11.1

4/23/2024, 2:56:50 PM

84%

rgb-fomo-ad-191

PERFORMANCE

LATENCY

51 ms of 100 ms

RAM

2433 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 5.1

4/23/2024, 2:56:55 PM

82%

rgb-fomo-ad-3dd

PERFORMANCE

LATENCY

35 ms of 100 ms

RAM

1209 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 5.1

4/23/2024, 2:54:56 PM

82%

rgb-fomo-ad-b76

PERFORMANCE

LATENCY

32 ms of 100 ms

RAM

1209 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 7.1

4/23/2024, 2:49:48 PM

82%

rgb-fomo-ad-775

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 | 5.1

4/23/2024, 2:49:50 PM

82%

rgb-fomo-ad-a29

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 | 7.1

4/23/2024, 2:43:44 PM

82%

rgb-fomo-ad-7e8

PERFORMANCE

LATENCY

35 ms of 100 ms

RAM

1209 kB of 8388608 kB

ROM

70 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

224 |

224

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.35 | 11.1

4/23/2024, 2:38:59 PM

82%

rgb-fomo-ad-cf6

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

4/23/2024, 2:34:22 PM

81%

rgb-fomo-ad-d2a

PERFORMANCE

LATENCY

62 ms of 100 ms

RAM

2433 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 7.1

4/23/2024, 2:43:41 PM

81%

rgb-fomo-ad-fc1

PERFORMANCE

LATENCY

41 ms of 100 ms

RAM

2433 kB of 8388608 kB

ROM

58 kB of 33554432 kB

DSP NN Unused

IMAGE INPUT

320 |

320

IMAGE

RGB

FOMO-AD (IMAGES)

MobileNetV2 0.1 | 11.1

4/23/2024, 2:37:40 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

Run #4:

Search space configuration

Configure the search space to narrow down your EON Tuner results. Include multiple parameter values to search for different combinations. To start, select a template below, a previous search space configuration, or add blocks manually.

Continuous motion

Classification

Detect cases where a motion is constantly playing.

Classification & anomaly

Detect cases where a motion is constantly playing

Motion events

Classification

Listen for short one-time occurances of motion

No previous runs available

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

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

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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?