Image classification is a fundamental artificial intelligence task that involves assigning one or more predefined labels to a digital image based on its visual content. It relies mainly on machine learning and deep learning algorithms that can extract relevant features from images to distinguish between them. Unlike object detection or image segmentation, image classification focuses solely on categorizing the entire image without precisely localizing objects or dividing regions. Typically, it utilizes convolutional neural networks (CNNs), which learn to identify hierarchical visual patterns. This technology enables systems to automatically recognize objects, scenes, or anomalies within images.
Use cases and application examples
Image classification is widely used in facial recognition for security, AI-assisted medical diagnostics (such as analyzing radiological images), automatic photo sorting, industrial monitoring (defect detection), and content management on social media (image moderation). It is also employed in precision agriculture to identify plant diseases and in automotive systems for recognizing traffic signs.
Main software tools, libraries, frameworks
Commonly used libraries and frameworks include TensorFlow, PyTorch, Keras, scikit-learn, FastAI, and OpenCV. These tools offer dedicated modules for building, training, and evaluating image classification models. Advanced cases often use pre-trained networks such as ResNet, Inception, VGG, or EfficientNet.
Recent developments, evolutions and trends
Recent research focuses on the efficiency of Transformer-type architectures (Vision Transformers, ViT), self-supervised learning, reducing model size for embedded applications, and improving robustness against bias and adversarial attacks. Additionally, automatic annotation and semi-supervised learning help decrease dependence on large labeled datasets, broadening application possibilities.