A computer vision model is an AI model specifically designed to perform tasks involving visual data – such as images or videos – and to output some interpretation of that data. In essence, it’s a mathematical or computational model that simulates aspects of human visual perception, enabling a computer to identify and categorize objects, people, or scenes in visual inputs​. For example, a computer vision model could be an image classifier that labels an input image as “landscape” or “portrait,” an object detector that finds the locations of dogs and cats in a photo, or a face recognition system that matches a face to a person’s identity. These models lie at the heart of computer vision applications and are the result of training algorithms on large collections of annotated visual data.Modern computer vision models are predominantly based on machine learning, especially deep learning. A common type is the convolutional neural network (CNN), which is well-suited for grid-structured data like images. CNN-based models automatically learn visual features (edges, textures, shapes, etc.) from the pixels, through layers of filters, rather than requiring manual feature engineering.For instance, in image classification, a CNN model (such as ResNet or VGG) will take pixel values as input and produce a probability distribution over classes as output; the model’s parameters are learned from a large labeled dataset (e.g., ImageNet) by optimizing to predict the correct labels. Other types of vision models include Fully Convolutional Networks (FCNs) or U-Net for segmentation (outputting pixel-wise class labels), region-based CNNs (like Faster R-CNN, YOLO, SSD) for object detection (outputting bounding boxes and classes), and more recently Vision Transformers for various vision tasks. There are also classical computer vision models (pre-deep-learning era) that use handcrafted features: e.g., a model that uses SIFT or HOG features fed into an SVM classifier. While these have largely been surpassed by deep learning models in accuracy, they are still conceptually useful and sometimes computationally cheaper for certain tasks.Crucially, a computer vision model must generalize from the examples it has seen to new images. Techniques like data augmentation (randomly perturbing training images) are used during training to help the model be invariant to translations, rotations, lighting changes, etc. A well-trained vision model can, for example, recognize a stop sign in various conditions (night or day, partially occluded, at an angle). The performance of vision models is often measured on benchmark datasets. For instance, a model’s accuracy on ImageNet (for classification) or mAP on COCO (for detection) is used to compare it with others. Many computer vision models also incorporate post-processing or domain-specific heuristics to refine outputs (for example, non-maximum suppression to remove duplicate detections in object detection). In summary, a computer vision model is the AI component that “understands” images – thanks to sophisticated learning algorithms, these models can achieve tasks like recognizing faces or segmenting medical images with high proficiency, transforming raw pixel data into meaningful decisions or labels​. As the field advances, vision models continue to improve, bridging the gap between human visual understanding and machine perception.