What is AutoML Vision?
AutoML Vision enables training custom computer vision models without machine learning expertise. The service supports image classification for categorizing entire images and object detection for localizing objects. You upload labeled images, Google trains an optimized model for your specific task.
Core Features
- Image Classification: Categorize images into custom classes
- Object Detection: Detect and localize multiple objects in an image
- Edge Export: Export models for TensorFlow Lite, Edge TPU and containers
- Integrated Labeling: Web interface for efficient image labeling
- Model Evaluation: Detailed metrics like precision, recall and confusion matrix
Typical Use Cases
Manufacturing Quality Control
Cameras in production automatically detect defects. The model learns from example images of defect-free and defective products and classifies in real-time.
Retail and Inventory
Automatic detection of products on shelves. The model identifies missing products, incorrect placements and stock levels.
Medical Image Analysis
Support for diagnosis through analysis of X-rays or pathology slides. The model is trained on clinically validated data.
Benefits
- Production-ready models without a data science team
- Edge deployment for offline scenarios and low latency
- Continuous improvement through retraining
- Integration with existing image pipelines
Integration with innFactory
As a Google Cloud Partner, innFactory supports you with AutoML Vision: data preparation, labeling strategy, model training, edge deployment and integration into production systems. We help evaluate whether pre-trained APIs or custom models are a better fit.
Available Tiers & Options
AutoML Vision
- No ML knowledge required
- Automatic training
- Edge deployment possible
- High training image requirements
Typical Use Cases
Technical Specifications
Frequently Asked Questions
What is AutoML Vision?
AutoML Vision enables training custom image classification and object detection models without ML expertise. The service automates model architecture, training and optimization.
How many images are needed for training?
For image classification, at least 100 images per category are recommended, ideally 1,000+. For object detection, at least 100 annotated bounding boxes per object type should be provided.
Can I run models on edge devices?
Yes, AutoML Vision can export models for edge deployment. Models run on TensorFlow Lite, Edge TPU or in containers on edge servers.
What is the difference from the Vision API?
The Vision API offers pre-trained models for general image analysis. AutoML Vision trains custom models on your specific images for better results on domain-specific tasks.
