---
updatedAt: 2026-04-27T02:02:16.000Z
agentTools:
projectIndex: https://docs.logmeal.com/llms.txt
---
# 🔴 Multiple Food Dishes Segmentation ⏱⭐ Detects every food item on the image and recognises it.
© Consumes your monthly credits. Limited amount of monthly calls, exceeding calls will be charged as extras.
(D) Limited amount of daily calls per user.
(S) Limited amount of calls per second.
Given an image, it segments and detects each food item or food region appearing on it. Then the most probable dishes are recognized for each of the regions separately. These dishes come with their names, the corresponding ids and the associated probabilities (confidence).
Note that the segmentation_results, which contain the bounding boxes and segments detected, might not be directly applicable to the uploaded
image size. The segmentation pixel values are scaled depending on the 'processed_image_size' values provided
for each of the images (the images might be sometimes resized depending on the needs of the food recognition
algorithms used).
**Geographic Personalization**: we recommend setting the `country` parameter associated to the end-user (APIUser) before applying any food recognition/segmentation call. See any `/modifyUserProfileInfo` endpoint for additional details.
If you have a LogMeal Recommend plan or higher we recommend using the /segmentation/complete/quantity endpoint, instead. It provides the same information in the current endpoint and furthermore it automatically estimates the food quantity present on the picture.
# OpenAPI definition
```json
{
"openapi": "3.0.3",
"info": {
"version": "2.1.0",
"title": "Food AI Engine API Documentation",
"description": "Please, refer to [https://docs.logmeal.com](https://docs.logmeal.com) to find the most up-to-date version of the Food AI Engine API documentation.",
"contact": {
"email": "support@logmeal.com"
}
},
"servers": [
{
"url": "https://api.logmeal.com",
"description": "Production"
}
],
"tags": [
{
"name": "Image-based Food Recognition",
"description": "Endpoints related to applying food recognition on images.
These endpoints are all limited to APIUser 🔴 tokens",
"externalDocs": {
"url": "https://docs.logmeal.com/docs/guides-features-several-dishes-recognition#/"
}
}
],
"paths": {
"/v2/image/segmentation/complete": {
"post": {
"tags": [
"Image-based Food Recognition"
],
"summary": "🔴 Multiple Food Dishes Segmentation ⏱⭐ Detects every food item on the image and recognises it.",
"description": "© Consumes your monthly credits. Limited amount of monthly calls, exceeding calls will be charged as extras.\n\n\n(D) Limited amount of daily calls per user.\n\n\n(S) Limited amount of calls per second.\n\n\n \n\n\nGiven an image, it segments and detects each food item or food region appearing on it. Then the most probable dishes are recognized for each of the regions separately. These dishes come with their names, the corresponding ids and the associated probabilities (confidence).\n\n \n\nNote that the segmentation_results, which contain the bounding boxes and segments detected, might not be directly applicable to the uploaded \nimage size. The segmentation pixel values are scaled depending on the 'processed_image_size' values provided \nfor each of the images (the images might be sometimes resized depending on the needs of the food recognition \nalgorithms used).\n\n\n \n\n**Geographic Personalization**: we recommend setting the `country` parameter associated to the end-user (APIUser) before applying any food recognition/segmentation call. See any `/modifyUserProfileInfo` endpoint for additional details.\n\n \n\n\nIf you have a LogMeal Recommend plan or higher we recommend using the /segmentation/complete/quantity endpoint, instead. It provides the same information in the current endpoint and furthermore it automatically estimates the food quantity present on the picture.\n",
"parameters": [
{
"in": "query",
"name": "language",
"schema": {
"type": "string",
"enum": [
"spa",
"cat",
"eng",
"ita",
"nld",
"fre",
"ger",
"tur",
"gre",
"heb"
]
},
"description": "Desired language for the response. It must be a three-letter ISO 639-2/T code. Defaults to the APIUser's assigned language or english.",
"required": false
}
],
"requestBody": {
"description": "Image sent in files",
"required": true,
"content": {
"multipart/form-data": {
"schema": {
"type": "object",
"properties": {
"image": {
"type": "string",
"format": "binary"
}
},
"required": [
"image"
]
}
}
}
},
"responses": {
"200": {
"description": "Token is valid and image is received and recognized. Recognition information is sent in response (JSON format)",
"content": {
"application/json": {
"schema": {
"type": "object",
"properties": {
"imageId": {
"type": "integer",
"example": 3,
"description": "ID of the uploaded image."
},
"processed_image_size": {
"type": "object",
"properties": {
"width": {
"type": "integer",
"example": 500,
"description": "Width of the processed image. Note that it might not match the uploaded image width. Use this information to interpret the segmentation polygon and the bounding box extracted by the food recognition algorithms."
},
"height": {
"type": "integer",
"example": 400,
"description": "Height of the processed image. Note that it might not match the uploaded image height. Use this information to interpret the segmentation polygon and the bounding box extracted by the food recognition algorithms."
}
}
},
"occasion": {
"type": "string",
"example": "dinner",
"description": "This parameter is only returned if you have a LogMeal Plan with Nutritional Information history enabled. It defines which occasion of the day the intake has been done.\nIf available, the occasions defined by your company. Otherwise the default occasions: One of ['breakfast', 'lunch', 'dinner', 'snack']"
},
"occasion_info": {
"description": "Contains information about the occasion.",
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {
"description": "Occasion id. Null if default occasion",
"type": "integer",
"example": 45
},
"translation": {
"description": "Translation to the language of the request if available. Not available with default occasions.",
"type": "string",
"example": "cena"
}
}
}
},
"foodType": {
"description": "Object containing the information of the most probable/relevant food type appearing on the image.",
"type": "object",
"properties": {
"id": {
"type": "integer",
"example": 2,
"description": "ID of the most probable/relevant foodType on the image."
},
"name": {
"type": "string",
"example": "drinks",
"description": "Name of the most probable/relevant foodType on the image."
}
}
},
"foodFamily": {
"description": "Array containing multiple dict entries for each predicted foodFamily, with their respective probabilities. To obtain the available food families refer to '/v2/dataset/foodGroups'.",
"type": "array",
"items": {
"type": "object",
"description": "Object containing information of an specific foodFamily, with its respective prediction results.",
"properties": {
"id": {
"type": "integer",
"example": 14,
"description": "ID of the foodFamily obtained from the recognition process."
},
"name": {
"type": "string",
"example": "meat",
"description": "Name of the foodFamily obtained from the recognition process."
},
"prob": {
"type": "number",
"format": "float",
"example": 0.5,
"description": "Probability obtained from the recognition process for the given foodFamily."
}
}
}
},
"model_versions": {
"description": "Object containing the model versions used for this prediction. Only the models that have been used during the call will be present in this response. In order to obtain all the models available you can refer to '/v2/version/allModels'.",
"type": "object",
"properties": {
"__MODEL_NAME__": {
"type": "string",
"example": "v0.7",
"description": "Version of the '__MODEL_NAME__' model used."
}
}
},
"segmentation_results": {
"description": "Array containing the results of the multiple food segmentation and recognition process. Each object in the array corresponds to one food item or food region detected on the image.",
"type": "array",
"items": {
"description": "Object containing the information of one of the food items or food regions found by the algorithms, with their respective probability generated by the recognition process. All the returned attributes containing coordinates are provided with respect to the sent image size. All coordinates have their origin (0,0) at the top-left pixel/corner of the image.",
"type": "object",
"properties": {
"food_item_position": {
"description": "Incremental position starting from 1 that identifies the food item in the segmentation_results list. This is useful for reporting specific information for each food item (see the endpoints /confirm/dish, /confirm/quantity and /confirm/ingredients)",
"type": "integer",
"example": 1
},
"center": {
"description": "Center for the detected food item (center of contained_bbox).",
"type": "object",
"properties": {
"x": {
"type": "integer",
"example": 162
},
"y": {
"type": "integer",
"example": 367
}
}
},
"contained_bbox": {
"description": "Coordinates of the bounding box that contains the detected food item.",
"type": "object",
"properties": {
"x": {
"type": "integer",
"example": 40
},
"y": {
"type": "integer",
"example": 231
},
"h": {
"type": "integer",
"example": 273
},
"w": {
"type": "integer",
"example": 245
}
}
},
"polygon": {
"description": "Specific polygon surrounding the detected food item. Format: [x1, y1, x2, y2, x3, y3, ..., xN, yN], by drawing lines from (x1, y1) to (x2, y2), ..., (xN-1, yN-1) to (xN, yN) and (xN, yN) to (x1, y1), the whole polygon can be drawn.",
"type": "array",
"items": {
"type": "integer"
},
"example": [
92,
231,
91,
232,
85,
232,
85,
233,
95,
231
]
},
"serving_size": {
"description": "Automatically estimated food quantity (in grams) for this region.",
"type": "number",
"format": "float",
"example": 324.453
},
"recognition_results": {
"description": "List of the most probable dishes/food labels present in the current food segment/region.",
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {
"description": "Unique ID of the detected food dish.",
"type": "integer",
"example": 1474
},
"name": {
"description": "Name of the detected dish.",
"type": "string",
"example": "mushroom cream"
},
"prob": {
"description": "Probability or confidence score for this food dish or label.",
"type": "number",
"format": "float",
"example": 0.9973669648170471
},
"foodType": {
"description": "Object containing the information of the food type associated to the detected food dish/label.",
"type": "object",
"properties": {
"id": {
"type": "integer",
"example": 2,
"description": "ID of the foodType."
},
"name": {
"type": "string",
"example": "drinks",
"description": "Name of the foodType."
}
}
},
"foodFamily": {
"description": "Array containing multiple dict entries for each foodFamily associated to the detected food dish/item.",
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {
"type": "integer",
"example": 14,
"description": "ID of one of the foodFamilies associated to the detected food dish/label."
},
"name": {
"type": "string",
"example": "meat",
"description": "Name of one of the foodFamilies associated to the detected food dish/label."
}
}
}
},
"subclasses": {
"description": "List of subclasses that are semantically related to the detected food dish/label.",
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {
"description": "Unique ID of the subclass.",
"type": "integer",
"example": 1474
},
"name": {
"description": "Name of the subclass.",
"type": "string",
"example": "mushroom cream"
},
"prob": {
"description": "Probability or confidence score for this subclass.",
"type": "number",
"format": "float",
"example": 0.9973669648170471
},
"foodType": {
"description": "Object containing the information of the food type associated to the subclass.",
"type": "object",
"properties": {
"id": {
"type": "integer",
"example": 2,
"description": "ID of the foodType."
},
"name": {
"type": "string",
"example": "drinks",
"description": "Name of the foodType."
}
}
},
"foodFamily": {
"description": "Array containing multiple dict entries for each foodFamily associated to the subclass.",
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {
"type": "integer",
"example": 14,
"description": "ID of one of the foodFamilies associated to the subclass."
},
"name": {
"type": "string",
"example": "meat",
"description": "Name of one of the foodFamilies associated to the subclass."
}
}
}
}
}
}
},
"nutri_score": {
"description": "Nutritional score associated to the recognized food item (this information is only provided if you have a Monitor plan or higher).",
"type": "object",
"properties": {
"nutri_score_category": {
"description": "The Nutri-Score category for the predicted dish. The parameter is only returned if the APIUser has access to the 'Recommend' plan. Check endpoint '/v2/score/{imageId}/nutriScore' for more information on Nutri-Score.",
"type": "string",
"example": "A",
"enum": [
"A",
"B",
"C",
"D",
"E"
]
},
"nutri_score_standardized": {
"description": "The standardized Nutri-Score values for the predicted dish. The parameter is only returned if the APIUser has access to the 'Recommend' plan. Check endpoint '/v2/score/{imageId}/nutriScore' for more information on Nutri-Score.",
"type": "integer",
"example": 70
}
}
}
}
}
}
}
}
}
}
},
"examples": {
"Recognition complete response example": {
"value": {
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes",
"prob": 0.495849609375
},
{
"id": 1,
"name": "meat",
"prob": 0.492919921875
}
],
"foodType": {
"id": 1,
"name": "food"
},
"imageId": 1365065,
"processed_image_size": {
"height": 400,
"width": 500
},
"model_versions": {
"drinks": "v1.0",
"foodType": "v1.0",
"foodgroups": "v1.0",
"foodrec": "v1.0",
"ingredients": "v1.0",
"ontology": "v1.0",
"segmentation": "v1.0"
},
"occasion": "breakfast",
"occasion_info": {
"translation": "almuerzo",
"id": 4
},
"segmentation_results": [
{
"center": {
"x": 162,
"y": 367
},
"contained_bbox": {
"h": 273,
"w": 245,
"x": 40,
"y": 231
},
"food_item_position": 1,
"polygon": [
92,
231,
91,
232,
96,
233,
96,
232,
95,
231
],
"recognition_results": [
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
},
{
"id": 11,
"name": "fried food"
},
{
"id": 20,
"name": "tubers and derivatives"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 306,
"name": "french fries",
"nutri_score": {
"nutri_score_category": "A",
"nutri_score_standardized": 77
},
"prob": 0.3131542097331639,
"subclasses": [
{
"foodFamily": [
{
"id": 11,
"name": "fried food"
},
{
"id": 20,
"name": "tubers and derivatives"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 2266,
"name": "sweet potato fries",
"nutri_score": {
"nutri_score_category": "A",
"nutri_score_standardized": 73
},
"prob": 0.12463889521299051
}
]
},
{
"foodFamily": [
{
"id": 11,
"name": "fried food"
},
{
"id": 20,
"name": "tubers and derivatives"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 2266,
"name": "sweet potato fries",
"nutri_score": {
"nutri_score_category": "A",
"nutri_score_standardized": 73
},
"prob": 0.23145949427455853,
"subclasses": []
},
{
"foodFamily": [
{
"id": 11,
"name": "fried food"
},
{
"id": 20,
"name": "tubers and derivatives"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 892,
"name": "chips potatoes",
"nutri_score": {
"nutri_score_category": "C",
"nutri_score_standardized": 63
},
"prob": 0.16711932300418259,
"subclasses": []
},
{
"foodFamily": [
{
"id": 3,
"name": "dairy products"
},
{
"id": 8,
"name": "vegetables/legumes"
},
{
"id": 11,
"name": "fried food"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 394,
"name": "poutine",
"nutri_score": {
"nutri_score_category": "C",
"nutri_score_standardized": 59
},
"prob": 0.15727056819744548,
"subclasses": []
},
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
},
{
"id": 11,
"name": "fried food"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 75,
"name": "onion rings",
"nutri_score": {
"nutri_score_category": "A",
"nutri_score_standardized": 73
},
"prob": 0.1309964047906497,
"subclasses": []
}
]
},
{
"center": {
"x": 385,
"y": 338
},
"contained_bbox": {
"h": 297,
"w": 251,
"x": 260,
"y": 190
},
"food_item_position": 2,
"polygon": [
430,
190,
428,
191,
425,
191,
458,
191,
457,
190
],
"recognition_results": [
{
"foodFamily": [
{
"id": 1,
"name": "meat"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 903,
"name": "grilled lamb",
"nutri_score": {
"nutri_score_category": "C",
"nutri_score_standardized": 66
},
"prob": 0.24450516063213096,
"subclasses": []
},
{
"foodFamily": [
{
"id": 1,
"name": "meat"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 285,
"name": "grilled entrecote",
"nutri_score": {
"nutri_score_category": "C",
"nutri_score_standardized": 66
},
"prob": 0.22743939640625688,
"subclasses": []
},
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 782,
"name": "grilled vegetables",
"nutri_score": {
"nutri_score_category": "B",
"nutri_score_standardized": 70
},
"prob": 0.18574178912919906,
"subclasses": [
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 615,
"name": "sauteed vegetables",
"nutri_score": {
"nutri_score_category": "A",
"nutri_score_standardized": 77
},
"prob": 0.20307953941806833
},
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 155,
"name": "sauteed spinach",
"nutri_score": {
"nutri_score_category": "A",
"nutri_score_standardized": 81
},
"prob": 0.20087265196795767
},
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 266,
"name": "sauteed mushrooms",
"nutri_score": {
"nutri_score_category": "A",
"nutri_score_standardized": 73
},
"prob": 0.3247904184124985
},
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
},
{
"id": 16,
"name": "nuts"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 552,
"name": "catalan spinach",
"nutri_score": {
"nutri_score_category": "A",
"nutri_score_standardized": 88
},
"prob": 0.18574178912919906
},
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
},
{
"id": 11,
"name": "fried food"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 642,
"name": "stir fried spinach",
"nutri_score": {
"nutri_score_category": "C",
"nutri_score_standardized": 66
},
"prob": 0.18574178912919906
},
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 936,
"name": "grilled chanterelles",
"nutri_score": {
"nutri_score_category": "A",
"nutri_score_standardized": 88
},
"prob": 0.3247904184124985
}
]
},
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 2208,
"name": "fried vegetables",
"nutri_score": {
"nutri_score_category": "A",
"nutri_score_standardized": 73
},
"prob": 0.1842937361332224,
"subclasses": [
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 462,
"name": "fried green peppers",
"nutri_score": {
"nutri_score_category": "D",
"nutri_score_standardized": 45
},
"prob": 0.1842937361332224
},
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 782,
"name": "grilled vegetables",
"nutri_score": {
"nutri_score_category": "B",
"nutri_score_standardized": 70
},
"prob": 0.6658790144078298
},
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 615,
"name": "sauteed vegetables",
"nutri_score": {
"nutri_score_category": "A",
"nutri_score_standardized": 77
},
"prob": 0.20307953941806833
},
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 155,
"name": "sauteed spinach",
"nutri_score": {
"nutri_score_category": "A",
"nutri_score_standardized": 81
},
"prob": 0.20087265196795767
},
{
"foodFamily": [
{
"id": 8,
"name": "vegetables/legumes"
}
],
"foodType": {
"id": 1,
"name": "food"
},
"hasNutriScore": true,
"id": 266,
"name": "sauteed mushrooms",
"nutri_score": {
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}
},
"x-build": {
"time": "2026-11-04T14:34:34.240869Z"
}
}
```