File size: 1,801 Bytes
c0551d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | # Image Classification
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.
## Data Preparation
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:
- The zip file should contain multiple folders (the classes), each folder should contain images of a single class.
- The name of the folder should be the name of the class.
- The images must be jpeg, jpg or png.
- There should be at least 5 images per class.
- There should not be any other files in the zip file.
- There should not be any other folders inside the zip folder.
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.
## Training
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.
 |