This is an object detection example alorithm that utilizes the open images dataset. For more information, take a look our deep dive tutorial. Exploring this algorithms source is a great place to start when creating your own Tensorflow powered Object Detector.

I/O

Input

{  
   "image": String,
   "output": String,
   "min_score": Float,
}

• image - (required) - a hosted image file, may be a web url (http, https) or a data connector URI (data://, s3://, etc).
• output - (optional) - the output data connector URI (data://, s3://, etc) for the resultant annotated image. If output is not provided, only the bounding box data is returned.
• min_score - (optional) - the minimum score threshold for bounding box annotations, if a prediction's confidence is less than this minimum, it's not returned in the results. _If min_score is not defined, it defaults to 0.5

Alternatively you can just pass a url directly to the algorithm as a string.

Output

{
    "image": String,
    "boxes": [
        {
            "coordinates": {
                "x0": Float,
                "y0": Float,
                "x1": Float,
                "y1": Float
            },
            "label": String,
            "confidence": Float
        },
    ...
    ]
}

• image - The bounding box annotated image (if output was defined) data connector URI.
• boxes - a list of all detected objects and their bounding boxes.
• coordinates - the absolute cartesian coordinates of the bounding box found in the specimen image.
• label - the predicted label/class for the detected object
• confidence - the confidence of the class prediction (0 -> 1)