Computer Vision

What Are Computer Vision Models?

Computer vision models give machines the ability to interpret and understand the visual world. They identify objects, understand scenes, read text, detect faces, estimate depth, and track motion in images and video streams. Applications powered by these models range from quality control cameras on factory floors to medical image analysis systems that flag anomalies in radiology scans. The field advanced rapidly in 2025–2026 with SAM 3 (Meta, November 2025) for universal segmentation and YOLO26 (Ultralytics, September 2025) as a unified five-task detection framework.

Core Computer Vision Tasks

• Object detection — locate and classify multiple objects in an image with bounding boxes
• Image segmentation — assign every pixel to an object class or a specific instance
• OCR — extract printed and handwritten text from documents, signs, and receipts
• Facial recognition — identify or verify individuals from face images or video
• Pose estimation — detect human body keypoints for fitness, gaming, and animation
• Depth estimation — infer 3D structure from a 2D image for AR and robotics

Current State-of-the-Art Models

YOLO26 (September 2025) unifies detection, segmentation, classification, pose, and oriented bounding box in one efficient architecture — the best choice for real-time edge deployment. SAM 3 (Meta, November 2025) enables promptable concept segmentation with memory-based tracking, ideal for interactive annotation tools. RF-DETR (Roboflow, March 2025) provides state-of-the-art real-time detection with simpler training pipelines. CLIP remains the standard for zero-shot image classification and image-text retrieval. EasyOCR and PaddleOCR offer the best free, open-source text extraction across 80+ languages.