CODEWITHSUNDEEP
pythontensorflowmachine-learninggoogle-cloud-platformgoogle-cloud-vision
mattsmith5
mattsmith5

Reputation: 1093

Google Cloud Vision AutoML and TensorFlow Object Detection Differences

What is the difference between Google Cloud Vision Auto ML and Google Tensor Flow?

https://cloud.google.com/vision/automl/docs/train

https://www.tensorflow.org/hub/tutorials/object_detection

They are both made from the same company, and conduct API object detection, allowing user to both train image models, allowing user to code in python. Does Google Cloud AutoML use Google Tensorflow under the hood, just in a Cloud model, so user does not need to download software?

Just trying to understand both components.

Google Cloud Auto ML

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Upvotes: 3

Views: 1351

Answers (1)

Ismail
Ismail

Reputation: 1078

Google Cloud AutoML Vision runs on Google Cloud Platform and Google TensorFlow Object Detection runs on Google Colab.

There are few main differences. I will try to cover the most important ones here:

  1. Colab instance session times out after 12 hours where the maximum lifespan for a custom AutoML vision model is 18 months.

  2. Colab runs on Google Drive and its basic version is free where AutoML vision costs $3.15 per node hour.

Note: "You can try AutoML Vision Object Detection for free by using 40 free node hours each for training and online prediction, and 1 free node hour for batch prediction, per billing account."

  1. Colab easy authentication and has pre-built connectors where AutoML is fully integrated with Google Cloud Platform.

  2. Colab is a consumer product that doesn't have any enterprise support, where AutML vision has enterprise support to resolve all your issues and queries.

  • For the highest level of programming control, you are better off creating a custom object detection model using TensorFlow. Some examples can be found here. To reduce time to production, the customer can use transfer learning and use one of the available model architectures in TensorFlow Hub or download model weights from other sources.

  • AutoML Vision on the other hand doesn’t provide granular control over model building, so you won’t be able to tune the resulting model programmatically. AutoML uses Neural Architecture Search to design optimal neural network for the task at hand. This approach is faster since all that’s needed is training data and task to build a neural network for. You can find more details and examples on AutoML Vision.

  • If you want to use ready-made APIs (with zero customization to the model itself), they can directly leverage Vision API. The API serves predictions for common tasks (such as object detection, OCR, etc) and allows no control, without having to bring any data or train any ML model. The downside here is that the model behind the API is static and cannot be retrained like AutoML Vision. More details on the difference between Vision API and AutoML Vision can be found here.

  • Regarding what product does a better job of object detection - you should have to perform your own experiments and see which product fits your needs best.

Finally for Increasing order of programmatic control, it is as follows:

Vision API > AutoML Vision > TensorFlow (with transfer learning) > TensorFlow (custom model)

Upvotes: 2

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