How to Train Computer Vision Models Efficiently

How to Train Computer Vision Models Efficiently

The starting point of building a successful computer vision application is the model. This article is about three major components involved in training a model using the alwaysAI Model Training Toolkit

The COCO dataset is a popular open source dataset that we use as the basis of our transfer learning. It is computer vision specific and due to its wide variety classes and large size is the dataset commonly used for open-source computer vision models. With alwaysAI you can get all of the power of the COCO dataset tuned to your specific task or application. 

AlwaysAI provides access to pre-trained computer vision models as well as the ability to leverage transfer learning to train your own models. Using a pre-trained model can certainly expedite computer vision app development. However, pre-trained models can also impose limitations on what applications you can build. In order to build a unique, custom application, you are going to need to train your own model. Using transfer-learning enabled model re-training not only requires less training data but can also significantly cut down the time it takes to train a new model. 

There are three major components involved in training a model using the alwaysAI Model Training Toolkit:

  • Data Collection: Our Image Capture application helps you with your data collection, so that you can collect videos and images directly from your edge device. See this GitHub repo for more on this. 
  • Data Annotation: We have CVAT, a data annotation tool, built right into our Model Training Toolkit so that you can easily annotate your data. 
  • Transfer Learning: As mentioned earlier, train a custom MobileNet SSD model using transfer learning to take the knowledge of the COCO dataset and improve upon it or modify it to your unique dataset.

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