We're excited to share LandingLens now supports Classification Projects! Overall this project type has few differences from Object Detection and Segmentation however there are some key differences to this type that present unique advantages for users looking to train models quickly.
Classification allows users to quickly get to model building and deployment. Our Classified Folder Upload feature enables you to upload pre-classified images, meaning there is no need to go through long labeling tasks.
- Simply organize your images into folders by class. Note the folder name will be used to create a defect in your defect book on upload.
From your data browser, select Upload Images at the top right, you'll be presented with two options.
- Unclassified media uploads will be raw status, you can classify them using the platform labeling tool
- Classified media uploads will be classified based on you folder structure
From either upload page, you can either drag your folders/media into the upload area or click the area to select folders from your finder window
When uploading Classified folders of media, after adding folders you will see a summary of the images to be uploaded as well as the Defect names to be added. As you can see in this example, all of the class types are new and will create new classes in the defect book. Scroll through to review your media & click upload.
Note, you can now collapse the upload window at the top right and do other work on LandingLens while your images upload in the background.
After uploading your images will appear in your defect book. If you uploaded unclassified media you can create labeling tasks as usual. Instead of labeling localized regions of images, task assignees will simply be asked to classify the image into one of the class buckets.
After labeling or uploading classified media, you will be able to see each image tagged with a class. Once you've classified enough media, you can split and export the data to the Model module.
Launching classification models is easy - LandingLens automatically sets model type to the latest classification model. Simply select your exported data set, adjust any hyperparameters you want and launch the training job.
Once your model run completes, you'll see your new classification model in the overview page. For Classification we use AUC or Area Under the Curve.
The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes.
The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes. When AUC = 1, then the classifier is able to perfectly distinguish between all the Positive and the Negative class points correctly. If, however, the AUC had been 0, then the classifier would be predicting all Negatives as Positives, and all Positives as Negatives.
When 0.5< AUC <1, there is a high chance that the classifier will be able to distinguish the positive class values from the negative class values. This is so because the classifier is able to detect more numbers of True positives and True negatives than False negatives and False positives.
When AUC=0.5, then the classifier is not able to distinguish between Positive and Negative class points. Meaning either the classifier is predicting random class or constant class for all the data points.
So, the higher the AUC value for a classifier, the better its ability to distinguish between positive and negative classes.
Error analysis is exactly the same as other project types except for the overview metrics and the image analysis section. In the analysis section, instead of seeing localized labels per image, you will see the specific class at the bottom right of each image.