Image-Classification

Vision Transformers and Object Recognition Vision transformers bring a fresh view to how machines recognize objects in images. Born from models designed for language, they use self-attention to relate all parts of an image to each other. When trained on large data, these models can match or exceed traditional convolutional approaches on many recognition tasks. The shift matters because it emphasizes global context, not just local patterns, which helps in scenes with occlusion, clutter, or unusual viewpoints. ...

Computer Vision: Building Visual Intelligence Computer vision is the science of letting machines see and understand the world. With cameras, sensors, and clever software, computers can identify objects, describe scenes, and even track movements. This field blends math, data, and practical ideas to help people perform tasks more efficiently, from organizing photos to guiding a robot. The goal is visual intelligence that works reliably in the real world. Think of vision as a processing pipeline: capture pixels, reduce noise, and reveal meaningful patterns. Simple tasks once used fixed rules, but many useful systems now learn from examples. The more diverse and high-quality the data, the better the model can handle new pictures from phones, streets, or labs. ...