Your First No-Code Smart App

Learn how to create an image classification app with Pallet in under 10 minutes, without code!
Introducing Pallet
The technology for building AI-enhanced applications is more accessible than ever before, but the process of actually building & distributing these 'smart apps' can still be a challenge.
Pallet is a no-code platform that enables you to quickly turn machine learning models into shareable apps, and access them anytime through a simple consistent interface.
In this tutorial you will use Pallet to create an image classification app just 10 minutes.
If you only want to explore Pallet, just download the app and check out the Explore section of this guide to see everything you can do.
Create an Image Classification App
Creating an image classification app with Pallet is overall just a quick 3-step process:
1.Create a new Project,
2.Upload your model assets, and
3.Tune your Project’s settings according to your model
All you need to get started is an image classification model and corresponding set of classification labels - a common pair of files we call model assets.
Don't worry if you don't have these assets ready. Use one of our example models to get started quickly.
Currently, Pallet supports TensorFlow Lite models trained for image classification, and will eventually support models and frameworks designed for a variety of different tasks.
In this tutorial, you will use the Pallet Web Console to deploy a model to your phone. In another tutorial we'll show you how to deploy models right from the Pallet app itself.
Let's start by preparing your model assets for deployment.
Prepare Your Model
To create a classification app with Pallet, you just need a TensorFlow Lite model and a corresponding list of labels.
If you already have these model assets, you're ready to deploy, and you can skip this step. If you don't have model assets on hand, get started with a pre-built model, or follow one of our tutorials on quickly building and training custom image classification models:
    I have a model ↓​
    Create a custom image classification model →​
    Download a pre-built image classification model →
    Examples below:
🤖 MobileNet
🌼 FlowerNet
🐶 Dog Breeds
👊 Rock-Paper-Scissors
Developed by Google, this model can classify 1,000 different objects - from umbrellas to volcanoes!
MobileNet.zip
12MB
Binary
Download MobileNet assets
Each download is zip file containing a TensorFlow Lite Model + Labels pair. When the download completes, unzip the model assets to your computer.
Classifies daisies, dandelions, roses, sunflowers, and tulips.
FlowerNet.zip
8MB
Binary
Download FlowerNet assets
Each download is zip file containing a TensorFlow Lite Model + Labels pair. When the download completes, unzip the model assets to your computer.
This model can distinguish between 120 different dog breeds.
Dog Breeds.zip
8MB
Binary
Download Dog Breeds assets
Each download is zip file containing a TensorFlow Lite Model + Labels pair. When the download completes, unzip the model assets to your computer.
This model can tell if you're throwing Rock, Paper, or Scissors from the classic game.
(For best results, take a picture of your hand in Rock, Paper, or Scissors formation over a clear plain surface.)
Rock-Paper-Scissors.zip
8MB
Binary
Download RPS assets
Each download is zip file containing a TensorFlow Lite Model + Labels pair. When the download completes, unzip the model assets to your computer.
Deploy Your Model With Pallet
1. Install Pallet from the Google Play Store  ​
​ 
    and Sign Up to create a new account
2. In your computer browser, visit app.palletml.com in a new tab and Log In to the account you just created.
3. Every model you deploy with Pallet belongs to a Project. Create a New Project for your model.
4. Choose a name for your project and click Create.
5. Browse or Drag & Drop the model assets that you prepared earlier, then click Upload.
    And that's it! Your model is now deployed.✅
​
6. Return to the Pallet app, navigate to your Profile 👤
, and pull to refresh your list of Projects.
Your new Project will appear. ✨
 
7. Tap your Project to open a detailed view. (You'll learn how to add details to your Project in another guide).
With Pallet, classification models for photos work out of the box, so you don't need to tune any settings for this project right now. 
8. Now Launch your app 🚀
 
(Skip the Tune Settings dialog by tapping Launch)
You can immediately start classifying photos by taking a picture with your camera, selecting a picture stored on your device, or submitting a link to an image.
9. Congratulations 🎉
 
In just a few minutes, you were able to turn an image classification model into a mobile app, without any code!
By deploying with Pallet, your model is infinitely scalable, easily upgradable, and ready to be shared with the world.
Up Next
Now that you've got the hang of deploying models with Pallet: 
Learn how to quickly share your model with any number friends, colleagues, and other users in just a few taps.
Share a Model
Learn how to train and deploy your own custom image classification model:
Create an Image Classifier
Create more classification apps from our pre-built models:
Download Pre-Built Models
Explore more of Pallet
Explore

Getting Started - Previous

Tutorials

Next - Deploy

Deploy a Model

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Outline

Introducing Pallet

Create an Image Classification App

Prepare Your Model

Deploy Your Model With Pallet

Up Next