Image Classification

How Image Classification Works

Image classification models are trained using supervised learning, where they are fed a vast number of images that have already been manually labeled with the correct class. During training, the neural network learns to identify patterns, textures, shapes, and color combinations associated with each category. This learning is achieved through a process called backpropagation, which adjusts the model's internal parameters, or weights, to minimize the difference between its predictions and the actual labels.

Modern classification models often use deep learning architectures with many layers. Early layers might learn to recognize simple features like edges and corners, while deeper layers combine these to identify more complex structures like eyes, wheels, or faces. The final layer of the network typically uses a softmax function to produce a probability score for each possible class. The class with the highest probability is chosen as the final prediction. Key to this process is feature extraction, where the model automatically learns the most informative features for the classification task.

Applications of Image Classification

Image classification is used across numerous industries to automate and scale visual recognition tasks. Two prominent examples include:

1. Medical Image Analysis: In healthcare, classification models analyze medical scans like X-rays, MRIs, and CT scans to help radiologists detect diseases. For instance, a model can be trained on thousands of mammograms to classify images as either "benign" or "malignant," assisting in the early detection of breast cancer. This can lead to faster diagnoses and reduced human error. For more on this, see the American Cancer Society's overview of AI in cancer detection.
2. Agriculture Technology: Farmers use image classification to monitor crop health from images captured by drones or satellites. A model can classify images of plant leaves into categories like "healthy," "diseased," or "pest-infested." This allows for targeted application of pesticides or fertilizers, improving crop yields and promoting sustainable farming. Learn more about how Ultralytics supports AI in agriculture.

Image Classification vs. Related Tasks

While closely related to other computer vision tasks, image classification has a distinct purpose. It is important to differentiate it from:

• Object Detection: This task goes a step further than classification. Instead of assigning one label to the entire image, object detection identifies multiple objects within an image and draws a bounding box around each one, also assigning a class label to each box. For example, it would not just label an image "street scene," but would locate and classify individual "cars," "pedestrians," and "traffic lights."
• Image Segmentation: This task provides the most detailed understanding of an image by classifying each pixel. There are different types, including semantic segmentation, which assigns a class to every pixel (e.g., all pixels belonging to cars are one color), and instance segmentation, which distinguishes between different instances of the same object (e.g., each car is a different color). This level of detail is critical for applications like autonomous vehicles.

In summary, classification tells you what is in an image, detection tells you what and where, and segmentation provides a detailed, pixel-level map of everything in the scene.

Image Classification With Ultralytics

While renowned for object detection, Ultralytics YOLO models also excel at image classification tasks. State-of-the-art models like YOLO11 can be easily trained or fine-tuned on custom datasets using the intuitive Ultralytics Python package or the no-code Ultralytics HUB platform.

Our documentation provides extensive resources, including model training tips and a detailed guide on how to use YOLO11 for image classification. Developers can leverage pre-trained models on benchmark datasets like ImageNet, CIFAR-100, and Caltech-101 or train a new model from scratch. For those interested in the latest advancements, resources like Papers With Code offer a comprehensive overview of top-performing models. You can also compare YOLO model performance on standard benchmarks. Frameworks like PyTorch and TensorFlow provide the foundation for building and training these models.