API reference/Mappls AI / ML
POSThybridexact public snapshot

Returns the Brands bounding box array for the inputs Images

/brand_detection

API will predict the bounding box co-ordinates(x1, y1, x2, y2) i.e (left most coordinate, right most coordinate) of the Brands in the given image.

Exact public source snapshot

This operation is retained because its version-labelled public specification differs structurally from another generation. It is comparison evidence, not a promise of current support, compatibility, entitlement, or availability.

Call the operation

POSThttps://aiml.mappls.com/brand_detection
Base URLhttps://aiml.mappls.com/
Authenticationbearer
Journey rolecommand
Content typesmultipart/form-data

Parameters

NameLocationTypeDescriptionExample
No explicit parameters are declared by the source operation.

Body schema

FieldTypeDescriptionExample
filerequired
string

Image file in .jpg, .jpeg and .png formats

example
Request body example
{
  "file": "example"
}

Build your request in eight languages

Change source-backed examples once, validate required inputs, and copy the synchronized cURL, JavaScript, Python, Java, C#, Go, PHP, or Ruby request. Credentials remain environment references, and this page never sends a provider request.

Local request composerCode stays local · fixtures are explicit
POSTRequest inputs
0 editable · 0 protected
This operation has no editable path or query parameters.
Generated in eight languages
Environment-backed credentials
POSThttps://aiml.mappls.com/brand_detection
Credential-free sandboxExecute this contract without a provider call
How fixtures work
Generated from your inputs
curl --request POST \
  --url 'https://aiml.mappls.com/brand_detection' \
  --header "Accept: application/json" \
  --header "Authorization: Bearer ${MAPPLS_ACCESS_TOKEN}" \
  --header "Content-Type: multipart/form-data" \
  --data-raw '{"file":"example"}'

Credential-safe by constructionSet MAPPLS_ACCESS_TOKEN only in your trusted runtime. Code generation stays local. Sandbox execution sends only bounded fixture inputs to this developer platform and never reads a credential or calls Mappls.

Send a production-shaped request

Examples use environment-backed access tokens, explicit timeouts where supported, status checks, and JSON bodies derived from the source schema. Replace sample identifiers and coordinates with values from your application.

POST /brand_detection
curl --request POST \
  --url 'https://aiml.mappls.com/brand_detection' \
  --header 'Authorization: Bearer YOUR_ACCESS_TOKEN' \
  --header 'Content-Type: application/json' \
  --data '{
  "file": "example"
}'

Responses

200

OK

400

Bad Request, User made an error while creating a valid request.

500

Internal server error.

200 response fields

FieldTypeDescriptionExample
Prediction
array<object>

No field description in the source contract.

example
Prediction[].x1
integer

No field description in the source contract.

1
Prediction[].y1
integer

No field description in the source contract.

1
Prediction[].x2
integer

No field description in the source contract.

1
Prediction[].y2
integer

No field description in the source contract.

1
Response example
{
  "Prediction": [
    {
      "x1": 1,
      "y1": 1,
      "x2": 1,
      "y2": 1
    }
  ]
}

Operate it safely

command

This operation creates or triggers durable state. Supply an idempotency strategy and persist the returned identifier before continuing.

Keep credentials and raw tokens out of source, client bundles, prompts, and logs.Record the operation ID, status, latency, retry count, and source contract version in traces.Classify 4xx responses before retrying; back off and add jitter for retryable 5xx or transport failures.Cache only when request inputs, authorization scope, and freshness requirements permit it.

Contract provenance

Source file
mappls-developer-platform/data/corpus/public-api-contracts/mappls-ai-apis/docs/custom/brand_detection.yaml
SHA-256
20759d3fde92064b580e491c0d5b8334e2d334d011e021ce6abb1bc2f0831d14
Normalization
Fields are parsed from OpenAPI. Operational guidance and language samples are generated and labeled separately.