Object Detection API

The Object Detection API delivers rapid and precise image object recognition using cutting-edge neural networks designed by machine learning experts, ensuring high performance and accuracy.

About the API:  

 

The Object Detection API offers a state-of-the-art solution for image object recognition, harnessing advanced neural networks meticulously developed by leading machine learning experts. This API excels in identifying and categorizing objects within images with remarkable speed and precision, making it ideal for a wide range of applications, from security surveillance and autonomous driving to e-commerce and augmented reality. By leveraging deep learning techniques, the API can handle complex image data, recognizing multiple objects in various contexts and conditions. It supports various image formats and integrates seamlessly with existing systems, providing developers with a powerful tool for enhancing their applications' visual intelligence capabilities. Whether you need to detect products in an online store, analyze video footage for security threats, or enable interactive AR experiences, the Object Detection API delivers reliable and high-performance object recognition, streamlining processes and improving user experiences.

 

What this API receives and what your API provides (input/output)?

Pass the image file that you want to recognize the object from. Receive the label and confidence score. 

 

What are the most common uses cases of this API?

  • Security Surveillance: The API can be utilized in security systems to monitor and analyze live video feeds, identifying potential threats such as unauthorized access, suspicious objects, or unusual activities, enhancing real-time security response.

  • Autonomous Driving: In self-driving cars, the API plays a crucial role in identifying and classifying objects on the road, such as pedestrians, other vehicles, traffic signs, and obstacles, ensuring safe and efficient navigation.

  • E-Commerce: Online retailers can use the API to automatically tag and categorize products in images, improving search functionality, product recommendations, and inventory management by recognizing items and their attributes.

  • Healthcare: In medical imaging, the API aids in diagnosing conditions by detecting anomalies in X-rays, MRIs, or CT scans, such as tumors, fractures, or other medical conditions, thus supporting accurate and timely diagnosis.

  • Augmented Reality (AR): The API enhances AR applications by detecting and tracking objects in real-time, enabling interactive experiences such as virtual object placement, real-time information overlays, and immersive gaming environments.

 

Are there any limitations to your plans?

Besides the number of API calls, there is no other limitation.

API Documentation

Endpoints


Given an input image, return a list of detected objects labels, confidence percentages and bounding boxes. Objects with confidence less than 0.3 (30%) are filtered out.



                                                                            
POST https://zylalabs.com/api/4394/object+detection+api/5400/detection
                                                                            
                                                                        

Detection - Endpoint Features

Object Description
Request Body [Required] File Binary
Test Endpoint

API EXAMPLE RESPONSE

       
                                                                                                        
                                                                                                                                                                                                                            [{"label": "boat", "confidence": "0.82", "bounding_box": {"x1": "85", "y1": "50", "x2": "621", "y2": "328"}}, {"label": "person", "confidence": "0.48", "bounding_box": {"x1": "528", "y1": "232", "x2": "541", "y2": "248"}}, {"label": "person", "confidence": "0.43", "bounding_box": {"x1": "563", "y1": "231", "x2": "573", "y2": "247"}}, {"label": "person", "confidence": "0.41", "bounding_box": {"x1": "203", "y1": "64", "x2": "217", "y2": "92"}}, {"label": "person", "confidence": "0.38", "bounding_box": {"x1": "556", "y1": "264", "x2": "572", "y2": "293"}}, {"label": "person", "confidence": "0.37", "bounding_box": {"x1": "558", "y1": "189", "x2": "578", "y2": "212"}}, {"label": "person", "confidence": "0.34", "bounding_box": {"x1": "229", "y1": "66", "x2": "240", "y2": "94"}}, {"label": "person", "confidence": "0.34", "bounding_box": {"x1": "203", "y1": "79", "x2": "216", "y2": "98"}}, {"label": "person", "confidence": "0.33", "bounding_box": {"x1": "155", "y1": "182", "x2": "173", "y2": "210"}}, {"label": "person", "confidence": "0.33", "bounding_box": {"x1": "231", "y1": "80", "x2": "241", "y2": "97"}}, {"label": "person", "confidence": "0.32", "bounding_box": {"x1": "536", "y1": "267", "x2": "554", "y2": "290"}}, {"label": "person", "confidence": "0.31", "bounding_box": {"x1": "492", "y1": "234", "x2": "510", "y2": "249"}}]
                                                                                                                                                                                                                    
                                                                                                    

Detection - CODE SNIPPETS


    curl --location 'https://zylalabs.com/api/4394/object+detection+api/5400/detection' \
    --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 Object Detection API REST 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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The API supports various image formats, including JPEG, PNG, BMP, and TIFF. It can process images of different resolutions, though higher quality images may yield more accurate results.

The API is designed to detect and recognize multiple objects within a single image. It returns bounding boxes for each identified object along with the corresponding classification labels and confidence scores.

The API delivers high accuracy in object detection, with precision and recall metrics varying based on the complexity of the scene and the quality of the input image. Regular updates and model improvements enhance performance over time.

The response time depends on the image size and the number of objects within the image. Generally, the API is optimized for low latency, providing results within a few hundred milliseconds for standard image sizes.

While the API comes pre-trained on large, diverse datasets, customization options are available. Users can fine-tune the model on their own datasets to improve accuracy for specific object categories relevant to their application.

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