CLIP Zero-Shot Classifier

This deep learning image classification model is built using CLIP (Contrastive Language-Image Pretraining) and works on identifying and classifying images based on the defined classes, labels, or any textual description given by the user. This model differs from the conventional image classification models where the set of categories needs to be pre-defined while training, and in the case of CLIP, the image is classified on the basis of the class definitions provided by the user.

CLIP model analyzes the image based on the visual information present and the semantical information of the class names/descriptions provided by the user and assigns the most appropriate category to the image. The advantage of this model is that it can be used wherever the categories required for classification differ based on the project's requirements.

The model can classify images individually as well as in batches. Users can classify a single image or can use the model to classify multiple images automatically in a folder of images.

One of the important use cases of the proposed model is the classification of oriented imagery, such as imagery obtained through drones, airborne sensors, mobile mapping systems, or any other geospatial imagery source. Using the visual understanding of the image in combination with the class definition in text form allows users to classify imagery by objects, environment types, land use classes, features, or any other user-defined description.

Apart from the predicted class label, the model offers a probability score for every prediction made by the model, which allows users to understand how confident the model was that the image should belong to the selected class. Such confidence scores are useful both for filtering results and reviewing uncertain predictions, and also for automation purposes when integrating classifications in other processes.

Such an approach to image classification using CLIP proves to be valuable for the following fields, including AI-powered image analysis, remote sensing, GIS, drone imagery, aerial mapping, oriented imagery management, and computer vision. In comparison with conventional fixed-label classification models, the CLIP approach allows using user-defined textual classes, making the process of changing image classification workflows much more flexible than usual.