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 commitments. One click upgrade/downgrade or cancellation. No questions asked.
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.
Zyla API Hub is like a big store for APIs, where you can find thousands of them all in one place. We also offer dedicated support and real-time monitoring of all APIs. Once you sign up, you can pick and choose which APIs you want to use. Just remember, each API needs its own subscription. But if you subscribe to multiple ones, you'll use the same key for all of them, making things easier for you.
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