Topics covered:
  • Training a single label classification model
  • Analyzing model's performance
  • Using the model inside the platform or via REST API

You can download images used in this tutorial here

You can download video tutorial here

Basic workflow tutorial video tutorial

Uploading images and Image Labeling

Let’s start with uploading images. First, you need to click the big red button on the left labeled “Upload Images”, and then choose the images that you want. During the upload process, you can add the labels to tell the system what is inside the images. This is called image labeling.

Once uploading the images, you can adjust the labels using the panel on the left hand side.

Training a classification model

Once you are happy with the labels, you can begin to train your image classification model, either a single-label classification or multi-label classification model.

In the training menu, you can choose the model name and the training time, as well as a few advanced options.

When you are ready, press start to start your model training. During the training, you can track the progress of the training under “Explore Models/Predict” Menu. Note: this menu name was changed to “Trained models”.

Analyzing model performance
After the model is trained, you can analyse the model by clicking on ‘View training statistics’ from the “Trained models” menu. The statistics are divided into two sections, Train and Validation. On the validation tab, SentiSight provides both the global statistics, as well as the per class statistics, for Precision, Recall and F1. Experienced users might want to click ‘Advanced View’, which reveals more detailed statistics such as Learning curves and Confusion matrix. You can review the actual predictions by clicking on the ‘Show predictions’ button, which allows you to review each image individually, or sort by correct or incorrect predictions.
Using the model to make predictions

Once you are happy with your model, you can use it to start making new predictions. You can do this by either pressing the ‘Make a new prediction’ button on the models performance screen, or by selecting the model from the “Trained models” menu. Then, upload your new images to be predicted.

You can analyse your results either online, or the results can be downloaded in a JSON format. Even if you do not know any coding, you can download the images grouped by predicted label, which will download the images in separate folders by label, as a ZIP file.

Using the model inside the platform, or via the REST API
If you are a developer, you can use SentiSight via the REST API server to make predictions. For this, you will need the API token and Project ID, which can be found in the top right corner in the User Profile section. You will also need the model name of the model that you have trained