Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos. Using AutoTrain, its super-easy to train a state-of-the-art image classification model. Just upload a set of images, and AutoTrain will automatically train a model to classify them.
The data for image classification must be in zip format, with each class in a separate subfolder. For example, if you want to classify cats and dogs, your zip file should look like this:
cats_and_dogs.zip
├── cats
│ ├── cat.1.jpg
│ ├── cat.2.jpg
│ ├── cat.3.jpg
│ └── ...
└── dogs
├── dog.1.jpg
├── dog.2.jpg
├── dog.3.jpg
└── ...
Some points to keep in mind:
When train.zip is decompressed, it creates two folders: cats and dogs. these are the two categories for classification. The images for both categories are in their respective folders. You can have as many categories as you want.
Once you have your data ready, you can upload it to AutoTrain and select model and parameters.
If the estimate looks good, click on Create Project
button to start training.