Bring your own model (BYOM)

Upload your own model directly into your Edge Impulse project (TensorFlow SavedModel, ONNX, or TensorFlow Lite)

Bring your own model or BYOM allows you to optimize and deploy your own pretrained model (TensorFlow SavedModel, ONNX, or TensorFlow Lite) to any edge device, directly from your Edge Impulse project.

Getting Started

First, create a new project in Edge Impulse.

Also make sure you have your own pretrained model available locally on your computer, in one of the following formats: TensorFlow SavedModel (saved_model.zip), ONNX model (.onnx) or TensorFlow Lite model (.tflite)

For this guide, we will be uploading a pretrained image classification TFLite model for plant disease classification, downloaded from the TensorFlow Dev Hub.

Then, from the Dashboard of your Edge Impulse project under "Getting started", select Upload your model:

Edge Impulse project dashboard.

Step 1: Upload your model

1. Upload your trained model: Upload a TensorFlow SavedModel (saved_model.zip), ONNX model (.onnx) or TensorFlow Lite model (.tflite) to get started.

2. Model performance: Do you want performance characteristics (latency, RAM and ROM) for a specific device? Select "No" to show the performance for a range of device types, or "Yes" to run performance profiling for any of our available officially supported Edge Impulse development platforms.

Upload pretrained model - Step 1: Upload a model

After configuring the settings for uploading your model, select Upload your model and wait for your model to upload, you can check the upload status via the "Upload progress" section.

ONNX models

When selecting an ONNX model, you can also upload a .npy file to Upload representative features (Optional). If you upload a set of representative features - for example, your validation set - as an .npy file we can automatically quantize this model for better on-device performance.

Uploading a pretrained .onnx model

Step 2: Process your model

Depending on the model you have uploaded in Step 1, the configuration settings available for Step 2 will change.

For this guide, we have selected the following configuration model settings for optimal processing for an image classification model with input shape (300, 300, 3) in RGB format, Classification model output and 16 output labels: Tomato Healthy, Tomato Septoria Leaf Spot, Tomato Bacterial Spot, Tomato Blight, Cabbage Healthy, Tomato Spider Mite, Tomato Leaf Mold, Tomato_Yellow Leaf Curl Virus, Soy_Frogeye_Leaf_Spot, Soy_Downy_Mildew, Maize_Ravi_Corn_Rust, Maize_Healthy, Maize_Grey_Leaf_Spot, Maize_Lethal_Necrosis, Soy_Healthy, Cabbage Black Rot

After configuring your model settings, select Save model to view your model's on-device performance information for both MCUs and microprocessors (if applicable, depending on your model's arena size).

Step 2: Process your model

Check model behavior

Optionally upload test data to ensure correct model settings and proper model processing:

Step 2: Check model behavior

Step 2: Check model behavior results