Image Labeling API

About this Cloud Hub Solution:

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The Image Labelling API is a powerful tool that enables developers to automatically analyze and annotate images with labels and tags based on their content.

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With this API, developers can:

• Classify images into predefined categories, such as objects, scenes, and actions
• Detect and identify objects, faces, and textures within images
• Assign metadata and annotations to images, including captions and descriptions
• Customize labelling models and training data for specific use cases
• Access pre-trained models that can analyze images for objects, colors, food, explicit content, and other subjects

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Developers can use the Image Labelling API to build a wide range of applications that require image analysis, including:

• Computer vision and machine learning models that rely on accurate image classification and labelling
• Image search and retrieval systems that need to quickly and accurately locate specific images
• Content moderation and filtering platforms that require robust image analysis and classification
• Autonomous vehicles and robotics that rely on accurate image recognition and labelling
• Medical imaging and diagnostics that require precise image analysis and annotation

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The Image Labelling API uses deep learning algorithms to analyze images for scenes, objects, and other content, returning keywords that provide insights into the visual content.

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‍Example:

Photo by Annie Spratton Unsplash

cURL command to analyze this photo for labeling is:

curl --request GET \--url 'https://api.apilayer.com/image_labeling/url?url=IMAGE PATH' \--header 'apikey: YOUR API KEY'

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Response:

[
  {
    "label": "pantry",
    "confidence": 0.793
  },
  {
    "label": "room",
    "confidence": 0.793
  },
  {
    "label": "light brown color",
    "confidence": 0.642
  },
  {
    "label": "beige color",
    "confidence": 0.6
  },
  {
    "label": "utensil",
    "confidence": 0.592
  },
  {
    "label": "shelf",
    "confidence": 0.511
  },
  {
    "label": "support",
    "confidence": 0.511
  }
]