Training a custom image classifier in 5 minutes

In this tutorial, we'll guide you through the process of training a classifier using Multiple. Specifically, we'll use a dataset of screws to create a classifier that distinguishes between good and bad screws.

Follow these steps to achieve this:

Step 1: Open Multiple and create a new folder

Sign in to your Abraia account and navigate to the Multiple platform. In Multiple, create a new folder (related to your classification task) called "screws." This is where you'll organize your dataset and training-related files.

Step 2: Download the dataset and upload the images

Download the screws dataset and unzip the files to a location on your local machine. From the Multiple "screws" folder, click on the "upload" button. Select and upload the images from the unzipped dataset to the "screws" folder in Multiple.

Step 3: Annotate the screws images as good and bad

Navigate to the "screws" folder in Multiple. Select images that represent good screws clicking on the checkboxes in every thumbnail. After selecting the images, click on the "annotate" button, and enter "good" as label. Select the remaining images, that represent bad screws (those not annotated as good). Click on the "annotate" button again, and enter "bad" as new label.

Step 4: Configure, train, and save the classification model

Click on the "train" button to start the training process. A notebook interface will open. Input your User ID and the API Key in the notebook. These credential are necessary for accessing the Abraia's training resources.

Inside the notebook, locate the cell that handles the dataset variable. Change the dataset value to "screws". Click "run all" to execute the entire notebook, which includes loading the dataset, defining the model, and training.

Once the notebook finished execution, the trained model will be saved in the "screws" folder.

Step 5: Test the model in your own device with Multiple

Navigate to the "screws" folder and select the images you want to test. Then, double-click on one of the selected images to open the testing interface. Multiple will display the model's predictions, indicating whether the screws are classified as "good" or "bad".

Congratulations! You have successfully trained a classifier for screws using Multiple. This tutorial provides a basic overview, and you can further explore advanced features, optimize your model, and fine-tune parameters based on your specific use case. Refer to Multiple's contact email for more detailed information and features.