Learn how to prepare training data and labels for AutoML Vision to create custom label output for photos.
- So the first step in working with AutoML for vision…is working with your data. Now, there are a couple of…points to note about the data that is designed,…at this point, to work with this API.…First, data, recommended by Google, are images "from the…real world" and they specifically say not x-rays,…hand drawings, or receipts, in their documentation.…They also recommend high-quality images, and they say…do not use grainy images, such as surveillance photos.…Maximum is 30 megabytes per image.…
As of this recording, the file types supported are JPEG,…PNG, GIF, BMP, WEBP, TIFF, and ICO.…Now, let's take a look at the photos that I have prepared.…You can see I have a folder called "naturePhotos",…and I've created a zip from it.…Underneath that folder, I have child folders with the…intended label names. So, here I have "Flowers", "food",…"sky", "sunset", "trees", and "vista".…And again I'll remind you that you need to have a minimum…of 10 labeled images per category, or per label, in order…to build a basic model. So, I have my zip file ready to go…
- Hosting options: Serverless, containers, and virtual machines
- Enabling the GCP ML AIs
- Preparing data with Cloud Dataflow and Dataprep
- Modeling predictions for images, video, text to speech, and cloud translation
- Machine learning with AutoML
- Advanced machine learning and deep learning
- Machine learning architectures
Skill Level Intermediate
1. Machine Learning on Google Cloud Platform
2. Machine Learning API Services
3. Machine Learning with AutoML
Understand AutoML Vision4m 57s
4. Advanced Machine Learning
5. Machine Learning Architectures
Next steps1m 30s
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