Zin / Steel Defect Classification Public

Missing data in your test dataset. To generate test accuracy, upload test data, or perform train/test split on your existing dataset from your project dashboard

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

Arduino Portenta H7 (Cortex-M7 480MHz)

100 ms

1024 kB

2048 kB

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General

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Training output

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Click 'Run EON Tuner' to begin

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.

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.

Do more with EON Tuner

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

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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 family

Clock rate

| MHz

Max

Custom device name

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

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