Compare Custom Vision to alternative Azure services

The Azure AI Vision Image Analysis API, based on Florence foundational model, now supports custom models with few-shot learning capability. Image Analysis is a separate offering from Azure Custom Vision. Use this guide to compare the two services.

To migrate an existing Custom Vision project to the Image Analysis 4.0 system, see the Migration guide.

Custom model training

Instead of Custom Vision, you can use the Image Analysis 4.0 model customization feature to create custom image identifier models with the latest technology from Azure.

Areas	Custom Vision service	Image Analysis 4.0 service
Tasks	Image classification Object detection	Image classification Object detection
Base model	CNN	Transformer model
Labeling	Customvision.ai	AML Studio
Web Portal	Customvision.ai	Vision Studio
Libraries	REST, SDK	REST, Python Sample
Minimum training data needed	15 images per category	2-5 images per category
Training data storage	Uploaded to service	Customer’s blob storage account
Model hosting	Cloud and edge	Cloud hosting only, edge container hosting to come
AI quality	context Image classification (top-1 accuracy, 22 datasets) Object detection (mAP@50, 59 datasets) 2 shot 51.47 33.3 3 shot 56.73 37.0 5 shot 63.01 43.4 10 shot 68.95 54.0 full 85.25 76.6	context Image classification (top-1 accuracy, 22 datasets) Object detection (mAP@50, 59 datasets) 2 shot 73.02 49.2 3 shot 75.51 61.1 5 shot 79.14 68.2 10 shot 81.31 75.0 full 90.98 85.4
Pricing	Custom Vision pricing	Image Analysis pricing

Product recognition

Instead of using the Custom Vision product-on-shelves model, you can use Image Analysis 4.0 Product Recogntion feature to train custom models that recognize retail products on shelves.

Areas	Products on Shelves – Custom Vision	Product Recognition – Image Analysis API/Customization
Features	Custom product understanding	Image stitching & rectification, Pretrained product understanding, Custom product understanding, Planogram matching
Base model	CNN	Florence transformer model
Labeling	Customvision.ai	AML Studio
Web Portal	Customvision.ai	Vision Studio
Libraries	REST, SDK	REST, Python Sample
Minimum training data needed	15 images per category	2-5 images per category
Training data storage	Uploaded to service	Customer’s blob storage account
Model hosting	Cloud and edge	Cloud hosting only, edge container hosting to come
AI quality	context Top-1 accuracy, 14 datasets 1 shot (catalog) 29.4 2 shot 57.1 3 shot 66.7 5 shot 80.8 10 shot 86.4 full 94.9	context Top-1 accuracy, 14 datasets 1 shot (catalog) 86.9 2 shot 88.8 3 shot 89.8 5 shot 90.3 10 shot 91.0 full 95.4
Pricing	Custom Vision pricing	Image Analysis pricing