This Fashion API analyzes a picture and gives a list of possible clothes and accessories that could be present on that picture along with their probabilities.
Its algorithm is trained on the most common types of apparel and is suitable for various fashion-related applications as well as other goals for visual recognition.
This API is created by API4AI. We build our APIs on a completely cloud technology stack which provides full operability, scalability and stable uptime. Our sole goal is to create out-of-the-box self-contained AI solutions that can easily be integrated into any application with just a few simple steps.
Try our service via:
š¤ Telegram demo bot: https://t.me/a4a_fashion_bot
š Web demo: https://api4.ai/apis/fashion
Feel free to contact API4AI team if have any questions.
š© Email: hello@api4.ai
š¬ Telegram: https://t.me/a4a_support_bot
š Instagram: https://www.instagram.com/api4ai
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Service for fashion image classification.
The service classifies input image and responds with fashion classes and their probabilities.
METHOD | URL | DESCRIPTION |
---|---|---|
GET | /fashion/v1/version |
Get service version. |
POST | /fashion/v1/results |
Perform image analysis and get results. |
Returns an actual version of the service in format vX.Y.Z
where X is the version of API.
PROPERTY | DESCRIPTION |
---|---|
Endpoint | /fashion/v1/version |
Method | GET |
Query parameters | ā |
POST parameters | ā |
Examples
Request:
$ curl -X 'GET' 'https://fashion4.p.rapidapi.com/v1/version'
Response:
v1.5.0
Performs actual image analysis and responds with results.
PROPERTY | DESCRIPTION |
---|---|
Endpoint | /fashion/v1/results |
Method | POST |
Query parameters | - |
POST parameters | image , url |
Response schema
For responses with 200
HTTP code the type of response is JSON object with the following schema:
{
"results": [
{
"status": {
"code": ...,
"message": ...
},
"name": ...,
"md5": ...,
"entities": [
{
"kind": "classes",
"name": "fashion-classes",
"classes": {
...
}
}
]
}
]
}
Primary fields:
Name | Type | Description |
---|---|---|
results[].status.code |
string |
Status code of image processing: ok or failure . |
results[].status.message |
string |
Human readable explanation for status of image processing. |
results[].name |
string |
Original image name passed in request (e.g. my_image.jpg ). |
results[].md5 |
string |
MD5 sum of original image passed in request. |
results[].entities[].classes |
object |
Predicted classes and probabilities. |
Other fields that are not described above always have the same values.
Probabilities are float
values in range from 0.0
to 1.0
.
Passing image
Image can be passed by posting regular āmultipart form dataā in two alternative ways:
image
fieldurl
fieldImage must be a regular JPEG or PNG image (with or without transparency).
Usually such images have extensions: .jpg
, .jpeg
, .png
.
The service checks input file by MIME type and accepts the following types:
image/jpeg
image/png
The size of image file must be less than 16Mb
.
Examples
Request:
curl -X 'POST' 'https://fashion4.p.rapidapi.com/v1/results' -F 'image=@image.jpg'
Response:
{
"results": [
{
"status": {
"code": "ok",
"message": "Success"
},
"name": "image.jpg",
"md5": "6ea449c4645b8811eef1342040725687",
"entities": [
{
"kind": "classes",
"name": "fashion-classes",
"classes": {
"top, t-shirt, sweatshirt": 0.04409244656562805,
"outwear": 0.008208364248275757,
"vest": 0.3351793885231018,
"shorts": 0.009493917226791382
}
}
]
}
]
}
When client sends an image that can not be processed for some reason(s), the service responds with 200
code and returns JSON object in the same format as the format for successful analysis. In this case, the results[].status.code
will have failure
value and results[].status.message
will contain relevant explanation.
Example of possible reasons for the issue:
Example response for corrupted image:
{
"results": [
{
"status": {
"code": "failure",
"message": "Can not load image."
},
"name": "file.jpg",
"md5": "d41d8cd98f00b204e9800998ecf8427e",
"entities": []
}
]
}
Request size is limited by approximately 32Mb
.
When client sends request that exceeds this limit, the service responds with 413
code.
The typical reason for exceeding this limit is overly large image.
Taking into account additional HTTP overhead, we strongly recommend to not pass image files of size more than 16Mb
.
Example response for too big request:
Error: Request Entity Too Large
Your client issued a request that was too large.
When client sends a request without an image and url, the service responds with 422
code and returns JSON object.
Example response for request without image or url:
{"detail": "Missing image or url field."}