Pass the image file that you want to recognize the object from. Receive the label and confidence score.
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.
Besides the number of API calls, there is no other limitation.
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.
Detection - Endpoint Features
Object | Description |
---|---|
Request Body |
[Required] File Binary |
[{"label": "clock", "confidence": "0.4", "bounding_box": {"x1": "114", "y1": "83", "x2": "188", "y2": "159"}}, {"label": "clock", "confidence": "0.38", "bounding_box": {"x1": "9", "y1": "4", "x2": "496", "y2": "345"}}, {"label": "traffic light", "confidence": "0.38", "bounding_box": {"x1": "371", "y1": "132", "x2": "404", "y2": "156"}}, {"label": "traffic light", "confidence": "0.32", "bounding_box": {"x1": "375", "y1": "121", "x2": "406", "y2": "152"}}, {"label": "traffic light", "confidence": "0.32", "bounding_box": {"x1": "363", "y1": "134", "x2": "392", "y2": "155"}}]
curl --location 'https://zylalabs.com/api/4495/object+identification+api/5516/detection' \
--header 'Content-Type: application/json' \
--form 'image=@"FILE_PATH"'
Header | Description |
---|---|
Authorization
|
[Required] Should be Bearer access_key . See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
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.
The Detection endpoint returns a list of detected objects, each with a label, confidence score, and bounding box coordinates. This data allows users to understand what objects were identified in the input image.
The key fields in the response data include "label" (the name of the detected object), "confidence" (the likelihood of the detection being accurate), and "bounding_box" (coordinates defining the object's location in the image).
The response data is organized as a JSON array, where each object detected is represented as a JSON object containing its label, confidence score, and bounding box coordinates.
The Detection endpoint primarily accepts an image file as input. Additional parameters for customization may include image preprocessing options, though these specifics are not detailed in the current documentation.
Users can utilize the returned data by analyzing the labels and confidence scores to determine the presence and reliability of detected objects. The bounding box coordinates can be used for visual overlays or further processing in applications.
Typical use cases include security surveillance for threat detection, autonomous driving for obstacle recognition, e-commerce for product tagging, and healthcare for identifying anomalies in medical images.
Data accuracy is maintained through the use of advanced algorithms and regular model updates. The API filters out detections with confidence scores below 0.3 to ensure only reliable results are returned.
If the API returns partial or empty results, users should check the input image quality and ensure it meets the API's requirements. Implementing fallback logic to handle such cases can enhance application robustness.
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