Object Detection with Keras and Determined

Object Detection with Keras and Determined

Object Detection with Keras and Determined. Object Detection is an important task in computer vision. Using deep learning for object detection can result in highly accurate models, but developers can also run into several challenges. In this tutorial, you’ll start by installing Determined on AWS, and then modifying an off-the-shelf tf.keras object detection model to work with Determined. Next, you’ll train the model on a single GPU, and finally conduct a hyperparameter search. The model being used is based on the Object Detection with RetinaNet using Keras tutorial.

Object Detection is an important task in computer vision. Using deep learning for object detection can result in highly accurate models, but developers can also run into several challenges. First, deep learning models are very expensive to train — even using GPUs, modern object detection models can take many hours of computation to train from scratch. Managing those GPUs and running workloads on many GPUs in parallel gets complicated fast. Second, object detection models typically have many hyperparameters. While algorithms exist to tune hyperparameters automatically, applying those algorithms in practice requires running hundreds or thousands of training jobs and comparing the performance of all those models. Talk about complex!

Determined makes training deep learning models and tuning hyperparameters much easier. It can automatically schedule jobs onto a cluster of GPUs (on-prem or in the cloud) and includes state-of-the-art algorithms for efficient hyperparameter tuning. It provides a real-time dashboard to track and visualize the results, enabling you to get a clear picture of how your model is performing in real-time — as a model developer, you can quickly kick off a training job or an HP search, and Determined will take care of the rest.

Determined also makes it easy to utilize multiple GPUs simultaneously, which means you can train a single model more quickly (using distributed training) or search through a massive hyperparameter space in less time — without modifying your code or dealing with managing GPUs by hand. Determined lets you focus on model development, so you can go from an idea to a working model in production much more quickly.

There’s more to Determined than meets the eye! To learn about what else Determined can do, check out the docs!

In this tutorial, you’ll start by installing Determined on AWS, and then modifying an off-the-shelf tf.keras object detection model to work with Determined. Next, you’ll train the model on a single GPU, and finally conduct a hyperparameter search.

The model being used is based on the Object Detection with RetinaNet using Keras tutorial.

deep-learning object-detection keras machine-learning developer

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