README.md
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)