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
Raspberry Pi 4
100 ms
585 kB
585 kB
Filters
Status
DSP type
Network type
View
Data set
Precision
Sort
General
F1-score
Precision
Recall
PERFORMANCE
LATENCY
11 ms of
100 ms
RAM
738 kB of
4194304 kB
Exceeds target by 152 kB
ROM
586 kB of
33554432 kB
Exceeds target by 1 kB
DSP
NN
Unused
INPUT
160 | 160
IMAGE
RGB
ACCURACY
TRANSFER LEARNING (IMAGES)
0.0005 | 20
MobileNetV2 160x160 0.35
16
|
0.5
5/19/2022, 7:31:38 PM
PERFORMANCE
LATENCY
19 ms of
100 ms
RAM
670 kB of
4194304 kB
Exceeds target by 85 kB
ROM
1633 kB of
33554432 kB
Exceeds target by 1048 kB
DSP
NN
Unused
INPUT
96 | 96
IMAGE
RGB
ACCURACY
TRANSFER LEARNING (IMAGES)
0.0005 | 20
MobileNetV2 160x160 0.75
16
|
0.5
5/19/2022, 7:28:11 PM
PERFORMANCE
LATENCY
49 ms of
100 ms
RAM
30 kB of
4194304 kB
ROM
59 kB of
33554432 kB
DSP
NN
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 |
5/19/2022, 7:30:45 PM
Training output
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Click 'Run EON Tuner' to begin
