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
Cortex-M7 216MHz
100 ms
340 kB
1024 kB
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DSP type
Model type
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General
F1-score
Precision
Recall
PERFORMANCE
LATENCY
70 ms of
100 ms
RAM
684 kB of
340 kB
Exceeds target by 344 kB
ROM
219 kB of
1024 kB
DSP
NN
Unused
IMAGE INPUT
160
|
160
IMAGE
Grayscale
ACCURACY (KERAS-TRANSFER-IMAGE)
TRANSFER LEARNING (IMAGES)
0.0005 | 20
MobileNetV2 0.1
16
|
0.1
|
9/10/2022, 8:44:46 AM
PERFORMANCE
LATENCY
297 ms of
100 ms
Exceeds target by 197 ms
RAM
51 kB of
340 kB
ROM
36 kB of
1024 kB
DSP
NN
Unused
IMAGE INPUT
64
|
64
IMAGE
RGB
ACCURACY (KERAS)
CLASSIFICATION (KERAS)
0.0005 | 10
| Type | Filters | Kernel | Rate |
|---|---|---|---|
| conv2d | 8 | 3 | - |
| conv2d | 16 | 3 | - |
| dropout | - | - | 0.25 |
9/10/2022, 8:45:13 AM
PERFORMANCE
LATENCY
637 ms of
100 ms
Exceeds target by 537 ms
RAM
674 kB of
340 kB
Exceeds target by 334 kB
ROM
231 kB of
1024 kB
DSP
NN
Unused
IMAGE INPUT
160
|
160
IMAGE
RGB
ACCURACY (KERAS-TRANSFER-IMAGE)
TRANSFER LEARNING (IMAGES)
0.0005 | 20
MobileNetV2 0.05
64
|
0.5
|
9/10/2022, 8:47:58 AM
Training output
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