Select a domain for a Custom Vision project
From the settings blade for your Custom Vision project, you can select a domain for your project. Choose the domain that is closest to your scenario.
|Generic||Optimized for a broad range of image classification tasks. If none of the other domains are appropriate, or you're unsure of which domain to choose, select the Generic domain.|
|Food||Optimized for photographs of dishes as you would see them on a restaurant menu. If you want to classify photographs of individual fruits or vegetables, use the Food domain.|
|Landmarks||Optimized for recognizable landmarks, both natural and artificial. This domain works best when the landmark is clearly visible in the photograph. This domain works even if the landmark is slightly obstructed by people in front of it.|
|Retail||Optimized for images that are found in a shopping catalog or shopping website. If you want high precision classifying between dresses, pants, and shirts, use this domain.|
|Compact domains||Optimized for the constraints of real-time classification on edge devices.|
|General||Optimized for a broad range of object detection tasks. If none of the other domains are appropriate, or you are unsure of which domain to choose, select the Generic domain.|
|Logo||Optimized for finding brand logos in images.|
|Products on shelves||Optimized for detecting and classifying products on shelves.|
|Compact domains||Optimized for the constraints of real-time object detection on edge devices.|
The models generated by compact domains can be exported to run locally. In the Custom Vision 3.4 public preview API, you can get a list of the exportable platforms for compact domains by calling the GetDomains API.
Model performance varies by selected domain. In the table below, we report the model size and inference time on Intel Desktop CPU and NVidia GPU . These numbers don't include preprocessing and postprocessing time.
|Task||Domain||Model Size||CPU inference time||GPU inference time|
|Classification||General (compact)||5 MB||13 ms||5 ms|
|Object Detection||General (compact)||45 MB||35 ms||5 ms|
|Object Detection||General (compact) [S1]||14 MB||27 ms||7 ms|
General (compact) domain for Object Detection requires special postprocessing logic. For the detail, please see an example script in the exported zip package. If you need a model without the postprocessing logic, use General (compact) [S1].
There is no guarantee that the exported models give the exactly same result as the prediction API on the cloud. Slight difference in the running platform or the preprocessing implementation can cause larger difference in the model outputs. For the detail of the preprocessing logic, please see this document.
 Intel Xeon E5-2690 CPU and NVIDIA Tesla M60