Car Make Model Generation Recognition API

API ID 14778

Car photo in, make, model and generation out, with years, body color and angle, in about 0.1 seconds. 3,824 car generations. Our own vision models, not an LLM: fast and the same answer every time.

API Documentation

Endpoints

Request
Send a photo of a car and get back its make, model and generation with production years, plus its body color and the angle it was photographed from, in about a tenth of a second. Recognition runs on our own vision models built for cars, not on a general AI model (LLM). On our servers it takes a median of 0.10 seconds for a photo with one car, and 90% of answers take under 0.14 seconds, not counting the upload; an LLM usually takes a few seconds per photo. That adds up when you check many photos or camera frames. With the same version, the same photo always gets the same answer, with fixed make, model and generation names that are easy to store and compare. For each car you get the top 3 make, model and generation guesses with their probabilities, the top 3 body colors, the angle (front, front-left, left, back-left, back, back-right, right or front-right), where the car is in the photo (a box), and whether the photo shows the car’s body, interior, dashboard, a wheel or a body part.
Endpoint ID: 38290
POST https://zylalabs.com/api/14778/car+make+model+generation+recognition+api/38290/detects+and+recognizes+vehicles+in+photo
INPUT PARAMETERS

Detects and recognizes vehicles in photo — Endpoint Features

Object Description
region Optional Including region parameter will lead to prioritizing most common brands in the region over those rarelyencountered. While this option helps to produce stronger recognition results for the most common brands and models in the region, rare car brands and models are likely to be incorrectly recognizedRegions currently supported:- CIS - Commonwealth of Independent States- EU - European Union- NA - North America
Request Body Required File Binary

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

region
API EXAMPLE RESPONSE
JSON
{
    "detections": [
        {
            "angle": [],
            "box": {
                "br_x": 0.7382,
                "br_y": 0.7271,
                "tl_x": 0.2115,
                "tl_y": 0.2993
            },
            "class": {
                "name": "car",
                "probability": 0.9807
            },
            "color": [],
            "mm": [],
            "mmg": [
                {
                    "generation_id": 19047,
                    "generation_name": "III",
                    "make_id": 79,
                    "make_name": "Peugeot",
                    "model_id": 1783,
                    "model_name": "3008",
                    "probability": 0.989,
                    "years": "2023-"
                }
            ],
            "status": {
                "code": 0,
                "message": "",
                "selected": true
            },
            "subclass": [
                {
                    "name": "vehicle",
                    "probability": 1
                }
            ]
        },
        {
            "angle": [],
            "box": {
                "br_x": 0.4228,
                "br_y": 0.5976,
                "tl_x": 0.0842,
                "tl_y": 0.3151
            },
            "class": {
                "name": "car",
                "probability": 0.9908
            },
            "color": [],
            "mm": [],
            "mmg": [],
            "status": {
                "code": 31,
                "message": "Box rejected. Box was not selected according to the currently active selection strategy: center",
                "selected": false
            },
            "subclass": []
        },
        {
            "angle": [],
            "box": {
                "br_x": 0.265,
                "br_y": 0.5691,
                "tl_x": 0.0587,
                "tl_y": 0.324
            },
            "class": {
                "name": "car",
                "probability": 0.3324
            },
            "color": [],
            "mm": [],
            "mmg": [],
            "status": {
                "code": 31,
                "message": "Box rejected. Box was not selected according to the currently active selection strategy: center",
                "selected": false
            },
            "subclass": []
        },
        {
            "angle": [],
            "box": {
                "br_x": 0.2197,
                "br_y": 0.4184,
                "tl_x": 0.0128,
                "tl_y": 0.2777
            },
            "class": {
                "name": "car",
                "probability": 0.5305
            },
            "color": [],
            "mm": [],
            "mmg": [],
            "status": {
                "code": 23,
                "message": "Box rejected. Height is 101 which is less than box_min_height 140",
                "selected": false
            },
            "subclass": []
        },
        {
            "angle": [],
            "box": {
                "br_x": 0.0528,
                "br_y": 0.3732,
                "tl_x": 0.0026,
                "tl_y": 0.2878
            },
            "class": {
                "name": "car",
                "probability": 0.574
            },
            "color": [],
            "mm": [],
            "mmg": [],
            "status": {
                "code": 22,
                "message": "Box rejected. Width is 64 which is less than box_min_width 180",
                "selected": false
            },
            "subclass": []
        }
    ],
    "is_success": true,
    "meta": {
        "classifier": 2266,
        "md5": "03D5541B58310E471EF556D597BF94CF",
        "parameters": {
            "box_max_ratio": 3.15,
            "box_min_height": 140,
            "box_min_ratio": 1,
            "box_min_width": 180,
            "box_select": "center",
            "features": [
                "mmg"
            ],
            "region": [
                "DEF"
            ]
        },
        "time": 0.082
    }
}
Detects and recognizes vehicles in photo — CODE SNIPPETS

    curl --location 'https://zylalabs.com/api/14778/car+make+model+generation+recognition+api/38290/detects+and+recognizes+vehicles+in+photo' \
    --header 'Content-Type: application/json' \ 
    --form 'image=@"FILE_PATH"'

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Car Make Model Generation Recognition API simply include your bearer token in the Authorization header.

Headers
Header Description
Authorization Required Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

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Starts at
$10,000/Year


  • Custom Volume
  • Custom Rate Limit
  • Specialized Customer Support
  • Real-Time API Monitoring

Overview

Car photo in, make, model and generation out, with years, body color and angle, in about 0.1 seconds. 3,824 car generations. Our own vision models, not an LLM: fast and the same answer every time.

Car Make Model Generation Recognition API FAQs

The API returns data about a car's make, model, generation, production years, body color, and the angle from which the photo was taken. It provides the top three guesses for each category along with their probabilities.

Key fields in the response include "detections," which contains details like "angle," "box" (bounding box coordinates), "class" (vehicle type), "color," and "mm" (make, model, generation details).

The response is structured as a JSON object with a "detections" array. Each detection includes fields for angle, bounding box, class, color, and make/model/generation information, allowing for easy parsing and analysis.

The endpoint accepts a required file upload parameter named `image` (JPEG, PNG, WEBP, GIF, BMP and TIFF, up to 12 MB.) and an optional query parameter `region`, which can be set to `DEF` for worldwide or `NA` for North America.

The API provides information on car make, model, generation, production years, body color, and the angle of the photo. It also indicates the location of the car in the image and whether it shows the body, interior, or other parts.

Users can customize requests by specifying the `region` parameter to limit results to North America or worldwide. They must also upload a single image file for detection.

Typical use cases include automotive applications like vehicle identification in insurance claims, car sales platforms, and mobile apps for car enthusiasts wanting to identify vehicles from photos.

Yes. Recognition runs on our own vision models built for cars, not on an LLM. On our servers it takes a median of 0.10 seconds for a photo with one car, and 90% of answers take under 0.14 seconds, not counting the upload. An LLM usually takes a few seconds per photo.

Yes, as long as the recognition version stays the same: the same make, model and generation, with fixed names, so answers are easy to store and compare. An LLM can word its answer differently each time.

We tested it on 1,000 random car photos from used-car websites and recognized 970+ of them correctly, an accuracy of 97+%. Results depend on your photos.

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