Deep Train a Model Using Clarifai_EfficientDet – Clarifai

Model efficiency has become increasingly important in computer vision. Clarifai_EfficientDet is built on a new family of object detectors that have been designed for scalable and efficient object detection.

The Clarifai_EfficientDet visual detector enables you to build deep-trained models that are lightweight and fast. Clarifai_EfficientDet makes a good choice for an algorithm to run on edge devices, trains quickly and keeps inference response time kept at a minimum.

Clarifai_EfficientDet uses a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Deep train with Clarifai_EfficientDet.

Create your App in Portal

To begin, you will need to create an app to contain your project. You can learn more about creating deep training apps here.

Upload Your Training Data

Upload and label your training data in Portal or use our API. You can use this upload script for your convenience:

https://gitlab.infra.cf.ai/clarifai/clarifai/-/blob/master/conf/dst/upload_dataset_to_app.py

Create Model

Once your app is created, you will need to create a new model. You can do this in model mode, or by clicking the "Create a Model" button on your app home screen.

Choose Visual Embedder

ArcFace template is a "Visual Detector" model type. Select Visual Detector from the "Create a Custom Model" menu.

Choose "Clarifai_EfficientDet", this gives you access to the Clarifai_EfficientDet model template.

Choose the hyper-parameter settings that you would like to use for your model and click create model. You can find information about available hyper-parameter settings here.

Train Your Model

Finally, you will need to train your model. Just click the "Train Model" button in the top right corner of the screen.

You can check the status of your model training job in Explorer. The blue progress bar on in the lefthand sidebar will indicate training progress. Deep training can take longer than transfer learning. You can learn more about managing deep training projects here.

Evaluation

Once the model is trained you will want to check the evaluations metrics. Click "evaluate" for the model version that you would like to view. Once evaluation is complete you will be able to view the results by clicking "view". Learn more about model evaluation here.

Upload Your Training Data

Create Model

Choose Visual Embedder

Train Your Model

Evaluation

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