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‎articles/ai-services/computer-vision/how-to/shelf-analyze.md

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---
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title: Analyze a shelf image using pretrained models
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titleSuffix: Azure AI services
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description: Use the Product Understanding API to analyze a shelf image and receive rich product data.
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description: Use the Product Recognition API to analyze a shelf image and receive rich product data.
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author: PatrickFarley
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manager: nitinme
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ms.service: azure-ai-vision
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# Shelf Product Recognition (preview): Analyze shelf images using pretrained model
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The fastest way to start using Product Recognition is to use the built-in pretrained AI models. With the Product Understanding API, you can upload a shelf image and get the locations of products and gaps.
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The fastest way to start using Product Recognition is to use the built-in pretrained AI models. With the Product Recognition API, you can upload a shelf image and get the locations of products and gaps.
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:::image type="content" source="../media/shelf/shelf-analysis-pretrained.png" alt-text="Photo of a retail shelf with products and gaps highlighted with rectangles.":::
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## Examine the response
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A successful response is returned in JSON. The product understanding API results are returned in a `ProductUnderstandingResultApiModel` JSON field:
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A successful response is returned in JSON. The product recognition API results are returned in a `ProductRecognitionResultApiModel` JSON field:
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```json
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{
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"imageMetadata": {
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"width": 2000,
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"height": 1500
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},
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"products": [
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{
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"id": "string",
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"boundingBox": {
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"x": 1234,
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"y": 1234,
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"w": 12,
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"h": 12
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},
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"classifications": [
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{
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"confidence": 0.9,
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"label": "string"
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}
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]
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}
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"ProductRecognitionResultApiModel": {
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"description": "Results from the product understanding operation.",
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"required": [
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"gaps",
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"imageMetadata",
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"products"
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],
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"gaps": [
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{
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"id": "string",
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"boundingBox": {
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"x": 1234,
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"y": 1234,
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"w": 123,
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"h": 123
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},
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"classifications": [
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{
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"confidence": 0.8,
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"label": "string"
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}
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]
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"type": "object",
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"properties": {
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"imageMetadata": {
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"$ref": "#/definitions/ImageMetadataApiModel"
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},
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"products": {
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"description": "Products detected in the image.",
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"type": "array",
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"items": {
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"$ref": "#/definitions/DetectedObject"
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}
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},
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"gaps": {
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"description": "Gaps detected in the image.",
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"type": "array",
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"items": {
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"$ref": "#/definitions/DetectedObject"
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}
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}
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]
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}
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}
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```
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See the following sections for definitions of each JSON field.
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### Product Understanding Result API model
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### Product Recognition Result API model
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Results from the product understanding operation.
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Results from the product recognition operation.
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| Name | Type | Description | Required |
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| ---- | ---- | ----------- | -------- |
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| `imageMetadata` | [ImageMetadataApiModel](#image-metadata-api-model) | The image metadata information such as height, width and format. | Yes |
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| `products` |[DetectedObjectApiModel](#detected-object-api-model) | Products detected in the image. | Yes |
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| `gaps` | [DetectedObjectApiModel](#detected-object-api-model) | Gaps detected in the image. | Yes |
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| `products` |[DetectedObject](#detected-object-api-model) | Products detected in the image. | Yes |
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| `gaps` | [DetectedObject](#detected-object-api-model) | Gaps detected in the image. | Yes |
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### Image Metadata API model
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| Name | Type | Description | Required |
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| ---- | ---- | ----------- | -------- |
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| `id` | string | ID of the detected object. | No |
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| `boundingBox` | [BoundingBoxApiModel](#bounding-box-api-model) | A bounding box for an area inside an image. | Yes |
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| `classifications` | [ImageClassificationApiModel](#image-classification-api-model) | Classification confidences of the detected object. | Yes |
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| `boundingBox` | [BoundingBox](#bounding-box-api-model) | A bounding box for an area inside an image. | Yes |
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| `tags` | [TagsApiModel](#image-tags-api-model) | Classification confidences of the detected object. | Yes |
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### Bounding Box API model
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| `w` | integer | Width measured from the top-left point of the area, in pixels. | Yes |
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| `h` | integer | Height measured from the top-left point of the area, in pixels. | Yes |
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### Image Classification API model
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### Image Tags API model
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Describes the image classification confidence of a label.
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| Name | Type | Description | Required |
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| ---- | ---- | ----------- | -------- |
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| `confidence` | float | Confidence of the classification prediction. | Yes |
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| `label` | string | Label of the classification prediction. | Yes |
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| `name` | string | Label of the classification prediction. | Yes |
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## Next steps
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In this guide, you learned how to make a basic analysis call using the pretrained Product Understanding REST API. Next, learn how to use a custom Product Recognition model to better meet your business needs.
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In this guide, you learned how to make a basic analysis call using the pretrained Product Recognition REST API. Next, learn how to use a custom Product Recognition model to better meet your business needs.
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> [!div class="nextstepaction"]
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> [Train a custom model for Product Recognition](../how-to/shelf-model-customization.md)

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