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

Getting started

Documentation
Forums

Student / Sorting Public

Clone this project

Student / Sorting Public

Target: Espressif ESP-EYE (ESP32 240MHz) 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

Squash, 96x96

Espressif ESP-EYE (ESP32 240MHz)

100 ms

4096 kB

4096 kB

Filters

Status

Pending 0

Running 0

Completed 10

Failed 0

DSP type

Grayscale 5

RGB 5

Model type

FOMO 10

View

Data set

Validation

Test

Variant

Sort

General

Accuracy

Latency

RAM

ROM

Last updated

F1-score

Precision

Recall

Run #3 - Squash, 96x96 (Feb 24 2024, 19:35:33, finished)

Run #5 - 160x160, trainingBG (Feb 25 2024, 11:35:06, finished) Run #4 - No Squash, 96x96 (Feb 24 2024, 19:52:18, finished) Run #3 - Squash, 96x96 (Feb 24 2024, 19:35:33, finished) Run #2 (Feb 24 2024, 16:23:42, finished) Run #1 (Feb 24 2024, 11:17:18, finished)

100%

rgb-fomo-e2d

PERFORMANCE

LATENCY

1662 ms of 100 ms

Exceeds target by 1562 ms

RAM

244 kB of 4096 kB

ROM

78 kB of 4096 kB

DSP NN Unused

IMAGE INPUT

96 |

96

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.01 | 30 | 100%

FOMO (MobileNetV2 0.35) | int8

2/24/2024, 7:44:23 PM

90%

rgb-fomo-1b3

PERFORMANCE

LATENCY

972 ms of 100 ms

Exceeds target by 872 ms

RAM

240 kB of 4096 kB

ROM

64 kB of 4096 kB

DSP NN Unused

IMAGE INPUT

96 |

96

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.01 | 30 | 90%

FOMO (MobileNetV2 0.1) | int8

2/24/2024, 7:41:49 PM

80%

grayscale-fomo-bb1

PERFORMANCE

LATENCY

1610 ms of 100 ms

Exceeds target by 1510 ms

RAM

243 kB of 4096 kB

ROM

78 kB of 4096 kB

DSP NN Unused

IMAGE INPUT

96 |

96

IMAGE

Grayscale

OBJECT DETECTION (IMAGES)

0.01 | 30 | 80%

FOMO (MobileNetV2 0.35) | int8

2/24/2024, 7:45:09 PM

80%

rgb-fomo-d1d

PERFORMANCE

LATENCY

1728 ms of 100 ms

Exceeds target by 1628 ms

RAM

244 kB of 4096 kB

ROM

78 kB of 4096 kB

DSP NN Unused

IMAGE INPUT

96 |

96

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.01 | 30 | 80%

FOMO (MobileNetV2 0.35) | int8

2/24/2024, 7:43:53 PM

80%

grayscale-fomo-a39

PERFORMANCE

LATENCY

1019 ms of 100 ms

Exceeds target by 919 ms

RAM

240 kB of 4096 kB

ROM

64 kB of 4096 kB

DSP NN Unused

IMAGE INPUT

96 |

96

IMAGE

Grayscale

OBJECT DETECTION (IMAGES)

0.01 | 30 | 80%

FOMO (MobileNetV2 0.1) | int8

2/24/2024, 7:40:08 PM

70%

grayscale-fomo-e8f

PERFORMANCE

LATENCY

1723 ms of 100 ms

Exceeds target by 1623 ms

RAM

243 kB of 4096 kB

ROM

78 kB of 4096 kB

DSP NN Unused

IMAGE INPUT

96 |

96

IMAGE

Grayscale

OBJECT DETECTION (IMAGES)

0.1 | 60 | 70%

FOMO (MobileNetV2 0.35) | int8

2/24/2024, 7:43:27 PM

60%

grayscale-fomo-783

PERFORMANCE

LATENCY

660 ms of 100 ms

Exceeds target by 560 ms

RAM

240 kB of 4096 kB

ROM

64 kB of 4096 kB

DSP NN Unused

IMAGE INPUT

96 |

96

IMAGE

Grayscale

OBJECT DETECTION (IMAGES)

0.1 | 60 | 60%

FOMO (MobileNetV2 0.1) | int8

2/24/2024, 7:42:58 PM

40%

rgb-fomo-9f4

PERFORMANCE

LATENCY

1099 ms of 100 ms

Exceeds target by 999 ms

RAM

240 kB of 4096 kB

ROM

64 kB of 4096 kB

DSP NN Unused

IMAGE INPUT

96 |

96

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.1 | 60 | 40%

FOMO (MobileNetV2 0.1) | int8

2/24/2024, 7:42:00 PM

10%

grayscale-fomo-c2b

PERFORMANCE

LATENCY

1008 ms of 100 ms

Exceeds target by 908 ms

RAM

240 kB of 4096 kB

ROM

64 kB of 4096 kB

DSP NN Unused

IMAGE INPUT

96 |

96

IMAGE

Grayscale

OBJECT DETECTION (IMAGES)

0.1 | 30 | 10%

FOMO (MobileNetV2 0.1) | int8

2/24/2024, 7:40:27 PM

rgb-fomo-2e8

PERFORMANCE

LATENCY

1435 ms of 100 ms

Exceeds target by 1335 ms

RAM

240 kB of 4096 kB

ROM

64 kB of 4096 kB

DSP NN Unused

IMAGE INPUT

96 |

96

IMAGE

RGB

OBJECT DETECTION (IMAGES)

0.1 | 30 | 0%

FOMO (MobileNetV2 0.1) | int8

2/24/2024, 7:39:59 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 between 1 to 32 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

160x160, trainingBG (Feb 25 2024), finished No Squash, 96x96 (Feb 24 2024), finished Squash, 96x96 (Feb 24 2024), finished Run #2 (Feb 24 2024), finished Run #1 (Feb 24 2024), finished

IMPULSE CONFIGURATION

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

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
[
  {
    "inputBlocks": [
      {
        "type": "image",
        "dimension": [[96, 96], [128, 128], [160, 160]],
        "resizeMode": ["fit-short"]
      }
    ],
    "dspBlocks": [
      {"type": "image", "channels": ["Grayscale", "RGB"]}
    ],
    "learnBlocks": [
      [
        {
          "type": "keras-object-detection",
          "model": [
            "fomo_mobilenet_v2_a01",
            "fomo_mobilenet_v2_a35"
          ],
          "augmentationPolicyImage": ["all", "none"],
          "learningRate": [0.1, 0.01],
          "trainingCycles": [30, 60]
        }
      ]
    ]
  }
]
 
 
הההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההה
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

Do more with EON Tuner

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

© 2025 EdgeImpulse Inc. All rights reserved

Show logs

WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW

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

Sign up to clone this project

You'll need a free Edge Impulse account to clone this project. Create an account to add your own data, modify models, and join the largest community 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?