Auto-tagging

Automatic assignment of relevant tags and keywords to the input image

Overview

Harnessing the power of visual assets is crucial for businesses across various industries. However, as digital libraries grow exponentially, manually assigning tags and keywords to images becomes a daunting and time-consuming task. That's where automatic tagging can help.

The Image tagging ML model is a powerful machine learning algorithm integrated into our service. It automatically analyzes the pixel content of images, extracts their features, and assigns relevant tags or keywords to facilitate improved organization and utilization of visual assets. With this model, businesses can unlock a new level of efficiency and productivity.

The process of AI tagging involves object detection techniques that detect objects, scenes, and concepts within the images. Based on this analysis, appropriate tags are assigned to each image, enabling proper asset management and content discovery.

Use cases

The AI tagging model can provide support for a wide range of typical use cases, including:

Efficient organization - Automatically assigned tags enable efficient organization of visual assets by categorizing them based on specific objects, scenes or concepts. Users can easily navigate through their image library and quickly locate the desired assets.
Accelerated search - Users can retrieve images based on generated tags or keywords. This accelerates the search process, making it effortless to find the required images in large collections.
Content Recommendation - Visually similar assets can be identified based on shared tags. Users can discover related content and leverage the full potential of their visual assets.
Visual content analysis - The model can aid in understanding visual trends, analyzing customer preferences and performing visual data analytics.

API endpoints

An up-to-date reference with all API endpoints is available here:

Example API responses

Input image

API response

PreviousClassification models NextBrand detect

Last updated 2 months ago

Overview

Use cases

The AI tagging model can provide support for a wide range of typical use cases, including:

Efficient organization - Automatically assigned tags enable efficient organization of visual assets by categorizing them based on specific objects, scenes or concepts. Users can easily navigate through their image library and quickly locate the desired assets.

Accelerated search - Users can retrieve images based on generated tags or keywords. This accelerates the search process, making it effortless to find the required images in large collections.

Content Recommendation - Visually similar assets can be identified based on shared tags. Users can discover related content and leverage the full potential of their visual assets.

Visual content analysis - The model can aid in understanding visual trends, analyzing customer preferences and performing visual data analytics.

Example API responses

Input image

API response

{
    "status": "success",
    "version": "2.9.3",
    "request_uuid": "9ee38368-afb9-4d53-9a91-0581ebf1723a",
    "sha1": "d7bd5990a75d996fb27ff25a7649f5b0748df0e3",
    "count": 10,
    "tags": [
        {
            "confidence": 93.844,
            "tag": {
                "en": "Bird"
            }
        },
        {
            "confidence": 90.226,
            "tag": {
                "en": "Beak"
            }
        },
        {
            "confidence": 85.26,
            "tag": {
                "en": "Gesture"
            }
        },
        {
            "confidence": 83.657,
            "tag": {
                "en": "Grass"
            }
        },
        {
            "confidence": 80.749,
            "tag": {
                "en": "Plant"
            }
        },
        {
            "confidence": 79.05,
            "tag": {
                "en": "Happy"
            }
        },
        {
            "confidence": 78.175,
            "tag": {
                "en": "Wing"
            }
        },
        {
            "confidence": 75.138,
            "tag": {
                "en": "Feather"
            }
        },
        {
            "confidence": 72.807,
            "tag": {
                "en": "Seabird"
            }
        },
        {
            "confidence": 72.791,
            "tag": {
                "en": "Water"
            }
        }
    ],
    "file_downloaded": "4345ba03-f633-5c59-9f6e-a8b602950000.jpg"
}

{
    "status": "success",
    "version": "2.9.3",
    "request_uuid": "7db96f16-8aaa-4274-a74d-7b927ab5d1d9",
    "sha1": "a1ed145a10339e4ba9a7ed8d56d058b877a9a98c",
    "count": 10,
    "tags": [
        {
            "confidence": 97.906,
            "tag": {
                "en": "Wheel"
            }
        },
        {
            "confidence": 97.475,
            "tag": {
                "en": "Tire"
            }
        },
        {
            "confidence": 94.934,
            "tag": {
                "en": "Vehicle"
            }
        },
        {
            "confidence": 92.089,
            "tag": {
                "en": "Car"
            }
        },
        {
            "confidence": 91.314,
            "tag": {
                "en": "Automotive tire"
            }
        },
        {
            "confidence": 90.513,
            "tag": {
                "en": "Automotive lighting"
            }
        },
        {
            "confidence": 89.459,
            "tag": {
                "en": "Automotive design"
            }
        },
        {
            "confidence": 88.105,
            "tag": {
                "en": "Motor vehicle"
            }
        },
        {
            "confidence": 87.549,
            "tag": {
                "en": "Alloy wheel"
            }
        },
        {
            "confidence": 84.563,
            "tag": {
                "en": "Bumper"
            }
        }
    ],
    "file_downloaded": "car.webp"
}