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
No name set
Vision
Arduino Portenta H7 (Cortex-M7 480MHz)
100 ms
512 kB
2048 kB
Filters
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General
F1-score
Precision
Recall
grayscale-conv2d-fd9
PERFORMANCE
LATENCY
100 ms
RAM
512 kB
ROM
2048 kB
Unused
INPUT
64 | 64
IMAGE
Grayscale
ACCURACY
CLASSIFICATION (KERAS)
0.0005 | 10
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| conv2d | 8 | 3 | - |
| conv2d | 16 | 3 | - |
| conv2d | 32 | 3 | - |
| conv2d | 64 | 3 | - |
| dropout | - | - | 0.5 |
rgb-mobilenetv2-1ba
PERFORMANCE
LATENCY
100 ms
RAM
512 kB
ROM
2048 kB
Unused
INPUT
96 | 96
IMAGE
RGB
ACCURACY
TRANSFER LEARNING (IMAGES)
0.0005 | 20
MobileNetV2 0.35
64
|
0.1
rgb-mobilenetv2-13f
PERFORMANCE
LATENCY
100 ms
RAM
512 kB
ROM
2048 kB
Unused
INPUT
160 | 160
IMAGE
RGB
ACCURACY
TRANSFER LEARNING (IMAGES)
0.0005 | 20
MobileNetV2 0.05
16
|
0.5
grayscale-mobilenetv2-87c
PERFORMANCE
LATENCY
100 ms
RAM
512 kB
ROM
2048 kB
Unused
INPUT
96 | 96
IMAGE
Grayscale
ACCURACY
TRANSFER LEARNING (IMAGES)
0.0005 | 20
MobileNetV2 0.35
16
|
0.1
grayscale-conv2d-9f0
PERFORMANCE
LATENCY
100 ms
RAM
512 kB
ROM
2048 kB
Unused
INPUT
32 | 32
IMAGE
Grayscale
ACCURACY
CLASSIFICATION (KERAS)
0.0005 | 10
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| conv2d | 16 | 3 | - |
| conv2d | 32 | 3 | - |
| conv2d | 64 | 3 | - |
| dropout | - | - | 0.25 |
Training output
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Click 'Run EON Tuner' to begin
