Image Recognition
AI capability to identify and classify objects, people, text, and other content within images. Powers applications from photo organisation to quality inspection.
In-Depth Explanation
Image recognition enables computers to identify and categorise visual content. Deep learning, particularly CNNs and Vision Transformers, has made this remarkably accurate.
Types of image recognition:
- Image classification: What's the main subject?
- Object detection: What and where are objects?
- Face recognition: Identifying individuals
- Scene recognition: Understanding environments
- OCR: Recognising text in images
- Fine-grained recognition: Distinguishing similar categories
Key architectures:
- CNNs: ResNet, EfficientNet, VGG
- Vision Transformers: ViT, DeiT, Swin
- Hybrid models: Combining CNN and transformer elements
- Multimodal: CLIP, connecting vision and language
Accuracy milestones:
- Human-level on ImageNet (2015)
- Superhuman on specific tasks
- Still challenged by adversarial examples
Business Context
Image recognition automates visual inspection, document processing, inventory management, and security - anywhere humans currently look at images to make decisions.
How Clever Ops Uses This
We deploy image recognition for Australian businesses in quality control, document processing, and visual search applications.
Example Use Case
"Quality control system identifying defects in manufactured products with 99.5% accuracy, catching issues human inspectors miss due to fatigue."