Home Decor Tagging
The Home Decor Tagging service provides categories, sub-categories and various tags for Home Decor product images.
Please, see page API Calls and Credits for exact prices for individual API calls. The Tagging uses a flow of complex recognition models to tag each image, that's why the cost is higher than a simple image classification.
See it in action ...
- We provide taxonomy (top categories, categories, features and tags)
- We are constantly improving this service. We are open to add more features/tags on you request! (depends on your budget)
- Our home decor tagging is easily customizable (rename the tags for your needs, creating new tags from other, ...) through your custom profile (contact us at sales@ximilar.com)
Language Translations - Tags
This service can return results (tags) translated in your language if needed, simply contact us at care@ximilar.com for more information and available translations. If some translation is not available than our team can build it for you.
Taxonomy
Here we provide our Home Decor taxonomy pdf document with example images and full taxonomy tree with all possible tags.
HOME DECOR TAXONOMY
API reference
The API follows the general rules of Ximilar API as described in Section First steps.
List of Top Categories and Categories: /v2/top_categories
https://api.ximilar.com/tagging/homedecor/v2/top_categories
curl --request GET \
--url https://api.ximilar.com/tagging/homedecor/v2/top_categories \
--header 'content-type: application/json'
CLICK TO SHOW JSON RESULT
{
"Furniture": [
"Furniture/Furniture Set",
"Furniture/Beds",
"Furniture/Relax",
"Furniture/Sitting",
"Furniture/Storage",
"Furniture/Tables"
],
"Accessories": [
"Accessories/Bathroom",
"Accessories/Decorative",
"Accessories/Dining & Cookware",
"Accessories/Lighting",
"Accessories/Mirrors",
"Accessories/Storage",
"Accessories/Textile"
]
}
Home Decor detection and tags endpoint: /v2/detect_tags
https://api.ximilar.com/tagging/homedecor/v2/detect_tags
Given a list of image records, this method returns detected objects on image with all the possible tags (similar to /tags endpoint) for largest object or picked one.
Things to note
- Please, note that the "_base64" data is not returned. Use "_id" or any other field to identify your image.
- Each of the tag contains probability of tag present on the image. All the tags are based on the main product type which is present in "Top category" and "Category" field. If you know these two fields, please specify it in each of the record.
- All the tags are stored in field "_tags", only tags with probability > 30 % are returned
- The field "_tags_simple" contains just list of strings (tag names) and is simpler than "_tags"
- The field "_tags" and "_tags_simple" are stored in the first detected object
- Maximum number of records/images that can be send to API in one request is 10.
Parameters:
-
records: list of photos to predict the tags for- must contain either of
_urlor_base64field - see section image data for details - each record can contain
Top CategoryandCategoryfield which defines the main-category and sub-category of the product on the photo; for some images, it's not clear what object is the main (e.g. a model with a t-shirt and pants) and thus it's recommended to send over this information about Top category and Category (if known). For example: t-shirt "Top Category": "Clothing" and "Category": "Clothing/Upper" - if you want to get tags for specific object, then send the request with
_objectsfield (list) with only this object
- must contain either of
-
aggregate_labels: (optional) aggregate tags for the whole batchtrueorfalse(defaultfalse)- if
true, the records is considered as a batch of several photos of one object and after tagging the images separately, the tagger aggregates their tags into one output list of tags for the whole batch. Tags of individual images are not modified by this aggregation.
-
profile: (optional) if you know the id of your custom tagging profile, then this profile will apply on the _tags and transform it (rename tags/features, add tags, remove tags, ...) language: (optional) (language code). For more info please contact us atcare@ximilar.com
$ curl https://api.ximilar.com/tagging/homedecor/v2/detect_tags -H "Content-Type: application/json" -H "Authorization: Token __API_TOKEN__" -d '{
"records": [
{ "_url": "https://images.ximilar.com/examples/homedecor_real/5_Unsplash.jpg" }
]
}'
Returns:
- HTTP error code 2XX, if the method was OK and other HTTP error code, if the method failed.
- Body of the response is a JSON object (map) with the following fields:
status- a JSON map with a status of the method processing. It contains these subfields:code- a numeric code of the operation status; it follows the concept of HTTP status codes (2XX, 4XX). Specific codes are described for each type of answer (or operation) (see below).text- a text describing the status code
records- JSON array with the input records, each record enriched by field "_objects", this endpoint always return at least one object (if none was detected then it is created artificially with size of whole image).
CLICK TO SHOW JSON RESULT
{
"records": [
{
"_url": "https://images.ximilar.com/examples/homedecor/11.webp",
"region": "generic",
"_status": {
"code": 200,
"text": "OK",
"request_id": "e5727342-d336-4da0-8a3f-312ecbeeee6a"
},
"_id": "f285cc24-076c-4dca-8bc6-1a33aaa24052",
"_width": 1035,
"_height": 1380,
"_objects": [
{
"name": "Furniture Set",
"id": "63b75a7f-af12-4996-9262-76b65e9efebb",
"bound_box": [
12,
300,
899,
1196
],
"prob": 0.39538151025772095,
"area": 0.5564321221031996,
"expand_by_bound_box": [
0,
211,
987,
1285
],
"Top Category": "Furniture",
"Category": "Furniture/Furniture Set",
"_tags": {
"Category": [
{
"name": "Furniture/Furniture Set",
"prob": 1,
"id": "3094d4da-3392-4c80-9e7d-fc6f8b59c0a4",
"pre-filled": true
}
],
"Subcategory": [
{
"prob": 0.95088,
"name": "Kitchen set",
"id": "12143686-bdf8-44ad-8ca3-1808939a1c90"
}
],
"Top Category": [
{
"name": "Furniture",
"prob": 1,
"id": "ff06c568-dd14-4fb7-9eae-1a0d3c1da115",
"pre-filled": true
}
]
},
"_tags_map": {
"Top Category": "Furniture",
"Category": "Furniture/Furniture Set",
"Subcategory": "Kitchen set"
},
"_tags_simple": [
"Furniture",
"Furniture/Furniture Set",
"Kitchen set"
]
}
]
}
],
"version": "",
"model_format": "",
"status": {
"code": 200,
"text": "OK",
"request_id": "e5727342-d336-4da0-8a3f-312ecbeeee6a",
"proc_id": "ae7ee797-e592-4d4b-bf88-58a1266fee3e"
},
"statistics": {
"processing time": 2.421678066253662
}
}
Detect all objects and tags: /v2/detect_tags_all
https://api.ximilar.com/tagging/homedecor/v2/detect_tags_all
Given a list of image records, this method returns detected objects on image with all the possible tags (similar to /tags endpoint) for all objects.
Things to note
- Please, note that the "_base64" data is not returned. Use "_id" or any other field to identify your image.
- Each of the tag contains probability of tag present on the image. All the tags are based on the main product type which is present in "Top category" and "Category" field.
- All the tags are stored in field "_tags", only tags with probability > 30 % are returned
- The field "_tags_simple" contains just list of strings (tag names) and is simpler than "_tags"
- The field "_tags" and "_tags_simple" are stored in the first detected object
- Maximum number of records/images that can be send to API in one request is 10.
Parameters:
-
records: list of photos to predict the tags for- must contain either of
_urlor_base64field - see section image data for details - each record can contain
Top CategoryandCategoryfield which defines the main-category and sub-category of the product on the photo; for some images, it's not clear what object is the main (e.g. a model with a t-shirt and pants) and thus it's recommended to send over this information about Top category and Category (if known). For example: t-shirt "Top Category": "Clothing" and "Category": "Clothing/Upper" - if you want to get tags for specific object, then send the request with
_objectsfield (list) with only this object
- must contain either of
-
profile: (optional) if you know the id of your custom tagging profile, then this profile will apply on the _tags and transform it (rename tags/features, add tags, remove tags, ...) language: (optional) (language code). For more info please contact us atcare@ximilar.com
$ curl https://api.ximilar.com/tagging/homedecor/v2/detect_tags_all -H "Content-Type: application/json" -H "Authorization: Token __API_TOKEN__" -d '{
"records": [
{ "_url": "https://images.ximilar.com/examples/homedecor/2.jpeg" }
]
}'
Returns:
- HTTP error code 2XX, if the method was OK and other HTTP error code, if the method failed.
- Body of the response is a JSON object (map) with the following fields:
status- a JSON map with a status of the method processing. It contains these subfields:code- a numeric code of the operation status; it follows the concept of HTTP status codes (2XX, 4XX). Specific codes are described for each type of answer (or operation) (see below).text- a text describing the status code
records- JSON array with the input records, each record enriched by field "_objects", this endpoint always return at least one object (if none was detected then it is created artificially with size of whole image).
CLICK TO SHOW JSON RESULT
{
"records": [
{
"_url": "https://images.ximilar.com/examples/homedecor/2.jpeg",
"region": "generic",
"_status": {
"code": 200,
"text": "OK",
"request_id": "3ada7775-a2f7-4577-8109-8f3111140e2b"
},
"_id": "49b6cea9-7683-488d-acdf-14bc4ea9416a",
"_width": 1024,
"_height": 887,
"_objects": [
{
"name": "Mirrors & Frames",
"id": "433fd8c0-3ad4-4a47-bb63-07760fb5fc3b",
"bound_box": [
343,
3,
496,
154
],
"prob": 0.7158277630805969,
"area": 0.025435764867531005,
"expand_by_bound_box": [
328,
0,
511,
169
],
"Top Category": "Accessories",
"Category": "Accessories/Mirrors & Frames",
"_tags": {
"Category": [
{
"id": "27dd7b55-78c4-45a2-8721-22e93d69f1c3",
"name": "Accessories/Mirrors & Frames",
"prob": 1.0
}
],
"Subcategory": [
{
"prob": 0.72697,
"name": "frame",
"id": "4851d369-1d77-4d69-9f0d-f3ea107d9307"
}
],
"Material": [
{
"prob": 0.96967,
"name": "Accessories/Textile",
"id": "a3ae94de-ea36-4851-b558-76e80cb626b2"
}
],
"Shape": [
{
"prob": 0.91281,
"name": "square",
"id": "6d6d96c5-0dc1-4b69-b606-a62cb605d99c"
}
],
"Top Category": [
{
"id": "7c042069-8e9e-4905-82c2-90ca5882ecab",
"name": "Accessories",
"prob": 1.0
}
]
},
"_tags_map": {
"Top Category": "Accessories",
"Category": "Accessories/Mirrors & Frames",
"Subcategory": "frame",
"Material": "Accessories/Textile",
"Shape": "square"
},
"_tags_simple": [
"Accessories",
"square",
"frame",
"Accessories/Textile",
"Accessories/Mirrors & Frames"
]
},
{
"name": "Textile accessories",
"id": "02240abc-a136-4230-8fea-f0054ab8eac3",
"bound_box": [
7,
0,
96,
447
],
"prob": 0.3932034969329834,
"area": 0.04379998414599774,
"expand_by_bound_box": [
0,
0,
104,
491
],
"Top Category": "Accessories",
"Category": "Accessories/Textile",
"_tags": {
"Category": [
{
"id": "a3ae94de-ea36-4851-b558-76e80cb626b2",
"name": "Accessories/Textile",
"prob": 1.0
}
],
"Subcategory": [
{
"prob": 0.73414,
"name": "curtains",
"id": "3fb62ae9-6832-416c-ae9c-7b5134992537"
}
],
"Type": [
{
"prob": 0.56914,
"name": "shower curtains",
"id": "f00c746a-14a1-40ec-b526-eb76eb5a32fb"
}
],
"Color": [
{
"prob": 0.81357,
"name": "multicolor",
"id": "f8e41e35-f0d7-4f53-a005-9333de67b7a5"
}
],
"Pattern": [
{
"prob": 0.74798,
"name": "floral",
"id": "ae9b5b1b-6379-4f9d-8a8c-c69f9d3108d0"
}
],
"Top Category": [
{
"id": "7c042069-8e9e-4905-82c2-90ca5882ecab",
"name": "Accessories",
"prob": 1.0
}
]
},
"_tags_map": {
"Top Category": "Accessories",
"Category": "Accessories/Textile",
"Subcategory": "curtains",
"Type": "shower curtains",
"Color": "multicolor",
"Pattern": "floral"
},
"_tags_simple": [
"curtains",
"Accessories",
"multicolor",
"floral",
"Accessories/Textile",
"shower curtains"
]
},
{
"name": "Tables",
"id": "f868ec40-6031-474a-9378-fc91cada8ee1",
"bound_box": [
109,
49,
1024,
887
],
"prob": 0.38681429624557495,
"area": 0.8441925908962796,
"expand_by_bound_box": [
18,
0,
1023,
886
],
"Top Category": "Furniture",
"Category": "Furniture/Table",
"_tags": {
"Category": [
{
"id": "18caa085-542b-4798-accc-d6845aacfc40",
"name": "Furniture/Table",
"prob": 1.0
}
],
"Subcategory": [
{
"prob": 0.46407,
"name": "office desk",
"id": "00ef9566-e3d0-4bb9-9986-0e88b69ab8f6"
}
],
"Top Category": [
{
"id": "ff06c568-dd14-4fb7-9eae-1a0d3c1da115",
"name": "Furniture",
"prob": 1.0
}
]
},
"_tags_map": {
"Top Category": "Furniture",
"Category": "Furniture/Table",
"Subcategory": "office desk"
},
"_tags_simple": [
"Furniture/Table",
"Furniture"
]
},
{
"name": "Relax",
"id": "5bc71c4e-edc2-4216-a9d5-9a45ec00ea0a",
"bound_box": [
0,
328,
200,
745
],
"prob": 0.37499773502349854,
"area": 0.09182109639233371,
"expand_by_bound_box": [
0,
287,
220,
786
],
"Top Category": "Furniture",
"Category": "Furniture/Relax",
"_tags": {
"Category": [
{
"id": "e029cce9-d91c-430b-9822-8fb1ab34e3bf",
"name": "Furniture/Relax",
"prob": 1.0
}
],
"Subcategory": [
{
"prob": 0.56544,
"name": "chaise lounge",
"id": "7eb35fc6-65a2-4e61-874f-6636eb6e1467"
}
],
"Color": [
{
"prob": 0.81902,
"name": "multicolor",
"id": "f8e41e35-f0d7-4f53-a005-9333de67b7a5"
}
],
"Top Category": [
{
"id": "ff06c568-dd14-4fb7-9eae-1a0d3c1da115",
"name": "Furniture",
"prob": 1.0
}
]
},
"_tags_map": {
"Top Category": "Furniture",
"Category": "Furniture/Relax",
"Subcategory": "chaise lounge",
"Color": "multicolor"
},
"_tags_simple": [
"chaise lounge",
"multicolor",
"Furniture/Relax",
"Furniture"
]
},
{
"name": "Mirrors & Frames",
"id": "433fd8c0-3ad4-4a47-bb63-07760fb5fc3b",
"bound_box": [
845,
12,
1018,
175
],
"prob": 0.3678346276283264,
"area": 0.03104632010992108,
"expand_by_bound_box": [
828,
0,
1023,
191
],
"Top Category": "Accessories",
"Category": "Accessories/Mirrors & Frames",
"_tags": {
"Category": [
{
"id": "27dd7b55-78c4-45a2-8721-22e93d69f1c3",
"name": "Accessories/Mirrors & Frames",
"prob": 1.0
}
],
"Subcategory": [
{
"prob": 0.8398,
"name": "mirror",
"id": "bad8686c-d7b0-4679-b914-76258d7fca23"
}
],
"Mirror": [
{
"prob": 0.74643,
"name": "mirror with wooden frame",
"id": "7efa9919-6487-4414-881d-fdf04950e9de"
}
],
"Shape": [
{
"prob": 0.70564,
"name": "square",
"id": "6d6d96c5-0dc1-4b69-b606-a62cb605d99c"
}
],
"Top Category": [
{
"id": "7c042069-8e9e-4905-82c2-90ca5882ecab",
"name": "Accessories",
"prob": 1.0
}
]
},
"_tags_map": {
"Top Category": "Accessories",
"Category": "Accessories/Mirrors & Frames",
"Subcategory": "mirror",
"Mirror": "mirror with wooden frame",
"Shape": "square"
},
"_tags_simple": [
"Accessories",
"square",
"mirror",
"mirror with wooden frame",
"Accessories/Mirrors & Frames"
]
},
{
"name": "Decorative accessories",
"id": "7250de14-6a7b-4576-95f9-34bfaa1a55be",
"bound_box": [
959,
505,
1018,
874
],
"prob": 0.36259496212005615,
"area": 0.023969269658962795,
"expand_by_bound_box": [
954,
469,
1023,
886
],
"Top Category": "Accessories",
"Category": "Accessories/Decorative",
"_tags": {
"Category": [
{
"id": "a507d8f7-9192-40db-a709-1ac790824cb9",
"name": "Accessories/Decorative",
"prob": 1.0
}
],
"Subcategory": [],
"Material": [
{
"prob": 0.47899,
"name": "wood",
"id": "bbe6e490-d135-424a-abd4-86da48d590d9"
},
{
"prob": 0.38898,
"name": "Accessories/Textile",
"id": "a3ae94de-ea36-4851-b558-76e80cb626b2"
}
],
"Color": [
{
"prob": 0.60392,
"name": "beige",
"id": "f64c0575-f1a9-42ef-abab-fed2006960ca"
}
],
"Top Category": [
{
"id": "7c042069-8e9e-4905-82c2-90ca5882ecab",
"name": "Accessories",
"prob": 1.0
}
]
},
"_tags_map": {
"Top Category": "Accessories",
"Category": "Accessories/Decorative",
"Material": "wood",
"Color": "beige"
},
"_tags_simple": [
"beige",
"Accessories",
"Accessories/Decorative"
]
}
]
}
],
"version": "2022-10-05 11:02:32.689233+00:00",
"model_format": "",
"status": {
"code": 200,
"text": "OK",
"request_id": "3ada7775-a2f7-4577-8109-8f3111140e2b",
"proc_id": "f6b2b598-feb2-4c5b-8541-f90cef96069f"
},
"statistics": {
"processing time": 0.8162112236022949
}
}
Tagging endpoint: /v2/tags
https://api.ximilar.com/tagging/homedecor/v2/tags
Given a list of image records, this method returns tags as predicted by the Home Decor Tagging services, together with probabilities (the level of certainty) for each of these tags.
Things to note
- Please, note that the "_base64" data is not returned. Use "_id" or any other field to identify your image.
- Each of the tag contains probability of tag present on the image. All the tags are based on the main product type which is present in "Top Category" field.
- All the tags are stored in field "_tags", only tags with probability > 30 % are returned
- The field "_tags_simple" contains just list of strings (tag names) and is simpler than "_tags"
- Maximum number of records/images that can be send to API in one request is 10.
Multiple images for one Home Decor product'?
Do you have multiple images for one home decor product?
- You can send
'aggregate_labels': truein the request and the labels will be aggregated for entire batch! Your results is then based on several images and not only one and it is stored_aggregation_simpleand_aggregationfields!
Parameters:
-
records: list of photos to predict the tags for- must contain either of
_urlor_base64field - see section image data for details - each record can contain
Top Categoryfield which defines the main-category of the product on the photo; for some images, it's not clear what object is the main and thus it's recommended to send over this information about Category (if known).
- must contain either of
-
aggregate_labels: (optional) aggregate tags for the whole batchtrueorfalse(defaultfalse)- if
true, the records is considered as a batch of several photos of one object and after tagging the images separately, the tagger aggregates their tags into one output list of tags for the whole batch. Tags of individual images are not modified by this aggregation.
-
profile: (optional) if you know the id of your custom tagging profile, then this profile will apply on the _tags and transform it (rename tags/features, add tags, remove tags, ...) language: (optional) (language code). For more info please contact us atcare@ximilar.com
$ curl https://api.ximilar.com/tagging/homedecor/v2/tags -H "Content-Type: application/json" -H "Authorization: Token __API_TOKEN__" -d '{
"records": [
{ "_url": "https://bit.ly/2IymQJv" },
{"_base64": "/9j/4AAQSkZJR...", "_id": "image_id"}
]
}'
Returns:
- HTTP error code 2XX, if the method was OK and other HTTP error code, if the method failed.
- Body of the response is a JSON object (map) with the following fields:
status- a JSON map with a status of the method processing. It contains these subfields:code- a numeric code of the operation status; it follows the concept of HTTP status codes (2XX, 4XX). Specific codes are described for each type of answer (or operation) (see below).text- a text describing the status code
records- JSON array with the input records, each record enriched by field "_tags" and "category"_aggregationand_aggregation_simpleifaggregate_labelsset to true in request
CLICK TO SHOW JSON RESULT
{
"records": [
{
"_url": "https://vize-images.s3.amazonaws.com/image/773e2ab1-2a0c-4524-9593-7395994faae1/46e875e4-e90e-4afa-aa4d-23b320ec6d7c.jpg",
"_status": {
"code": 200,
"text": "OK",
"request_id": "a24bc3ee-bbe2-40ff-b8a0-edeeb7d720c3"
},
"_width": 1000,
"_height": 1000,
"Top Category": "Furniture",
"Category": "Furniture/Sitting",
"_tags": {
"Category": [
{
"prob": 0.99709,
"name": "Furniture/Sitting",
"id": "c397d929-5e29-4722-a98a-13629ae65677"
}
],
"Subcategory": [
{
"prob": 0.96032,
"name": "Dining chair",
"id": "1ab17573-78c2-4ccf-840e-93df756a5193"
}
],
"Top Category": [
{
"prob": 0.99406,
"name": "Furniture",
"id": "ff06c568-dd14-4fb7-9eae-1a0d3c1da115"
}
]
},
"_tags_map": {
"Top Category": "Furniture",
"Category": "Furniture/Sitting",
"Subcategory": "Dining chair"
},
"_tags_simple": [
"Furniture",
"Dining chair",
"Furniture/Sitting"
]
}
],
"status": {
"code": 200,
"text": "OK",
},
"statistics": {
"processing time": 0.8858194351196289
}
}
Home Decor detection endpoint: /v2/detect
https://api.ximilar.com/tagging/homedecor/v2/detect
Given a list of image records, this method returns detected homedecor objects on image with their category.
Things to note
- For getting best results, use this endpoint if there are multiple homedecor items.
- Please, note that the "_base64" data is not returned. Use "_id" or any other field to identify your image.
- Homedecor objects, with exact location (bounding box) are stored in the '_objects' field.
- Each of the tag and object contains probability of presence on the image.
- All the tags are stored in field "_tags" within the first (largest) '_object', only tags with probability > 30 % are returned
- The '_tags' field is returned only for the object with largest area, not probability!
- The field "_tags_simple" contains just list of strings (tag names) and is simpler than "_tags"
- Maximum number of records/images that can be send to API in one request is 10.
- If you know the Top Category or Category, then please specify them in each of the record. This can improve the overall precision of returned results.
- You can also send the object, simple create field '_objects' with one object (you will need to specify bound_box and Top Category)
- This endpoint cannot aggregate results from multiple records.
Parameters:
records: list of photos to predict the tags for- must contain either of
_urlor_base64field - see section image data for details
- must contain either of
$ curl https://api.ximilar.com/tagging/homedecor/v2/detect -H "Content-Type: application/json" -H "Authorization: Token __API_TOKEN__" -d '{
"records": [
{ "_url": "https://bit.ly/2IymQJv" },
{"_base64": "/9j/4AAQSkZJR...", "_id": "image_id"}
]
}'
Returns:
- HTTP error code 2XX, if the method was OK and other HTTP error code, if the method failed.
- Body of the response is a JSON object (map) with the following fields:
status- a JSON map with a status of the method processing. It contains these subfields:code- a numeric code of the operation status; it follows the concept of HTTP status codes (2XX, 4XX). Specific codes are described for each type of answer (or operation) (see below).text- a text describing the status code
records- JSON array with the input records, each record enriched by field "_objects"
CLICK TO SHOW JSON RESULT
{
"records": [
{
"_url": "https://images.ximilar.com/examples/homedecor/8.jpg",
"_status": {
"code": 200,
"text": "OK",
"request_id": "86ca41c7-b760-48c1-84df-5f59a21cd5d6"
},
"_id": "ee920142-edb5-4545-a9f4-41a3e0397932",
"_width": 300,
"_height": 300,
"_objects": [
{
"name": "Textile accessories",
"id": "02240abc-a136-4230-8fea-f0054ab8eac3",
"bound_box": [
0,
67,
283,
287
],
"prob": 0.5390826463699341,
"area": 0.6917777777777778,
"expand_by_bound_box": [
0,
45,
299,
299
],
"Top Category": "Accessories",
"Category": "Accessories/Textile"
},
{
"name": "Relax",
"id": "5bc71c4e-edc2-4216-a9d5-9a45ec00ea0a",
"bound_box": [
224,
20,
300,
211
],
"prob": 0.4882190525531769,
"area": 0.1612888888888889,
"expand_by_bound_box": [
217,
1,
299,
230
],
"Top Category": "Furniture",
"Category": "Furniture/Relax"
}
]
}
],
"version": "2022-10-03 16:43:45.552643+00:00",
"model_format": "",
"status": {
"code": 200,
"text": "OK",
"request_id": "86ca41c7-b760-48c1-84df-5f59a21cd5d6",
"proc_id": "8b776662-7987-4b41-b0ca-4154bef2e842"
},
"statistics": {
"processing time": 0.1509261131286621
}
}
Feedback endpoint: /v2/feedback
https://api.ximilar.com/tagging/homedecor/v2/feedback
If you get some result from prediction endpoints (tags, ...) and you see some mistakes in assigned tags, feel free to send us an image via this endpoint. Please include in message field textual description what was wrong with analyzed result.
Parameters:
- must contain either of
urlorbase64with url path of image or base64 data of image messageas textual description of what was wrongly analyzed
curl --request POST \
--url https://api.ximilar.com/tagging/homedecor/v2/feedback \
--header 'Authorization: Token __API_TOKEN__' \
--header 'Content-Type: application/json' \
--data '{
"message": "__THE_MESSAGE__",
"base64": "__BASE64_IMAGE__"
}'
Returns:
- HTTP error code 200, if the method was OK and other HTTP error code, if the method failed.
- Body of the response is a JSON object (map) with the following fields:
status- with message
CLICK TO SHOW JSON RESULT
{
"status": "Your feedback has been submitted. Thank you!"
}