Vision Model
Neural network trained to process images and understand visual content—classify objects, detect faces, or describe scenes.
Think of it like
Teaching a kid to recognize animals in a picture instead of just reading a list of words.
Example
ResNet-50 classifies 1000 object categories from ImageNet; YOLO detects people and cars in real-time video.
How it actually works
Convolutional layers slide filters across images, learning textures and shapes. Hierarchical: early layers learn edges, middle layers learn parts, late layers learn objects. Pre-trained on ImageNet then fine-tuned. More data-hungry than text models; 100k labeled images is a large dataset.
For product teams
Opens product capabilities in computer vision: content moderation, visual search, autonomous driving features.
For engineers
Backbone (ResNet, EfficientNet, ViT) extracts image features; head (classification, detection, segmentation) performs task-specific prediction.
Related
- VLM — Foundation for.
- Language model — Complements.
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