BigML Bindings: 101 - Images Object DetectionΒΆ

Following the schema described in the prediction workflow, document, this is the code snippet that shows the minimal workflow to create a deepnet and produce a single prediction.

from bigml.api import BigML
# step 0: creating a connection to the service (default credentials)
api = BigML()
# step 1: creating a source from the data in your local
# "data/images/cats.zip" file, that contains a collection of images
# and an "annotations.json" file with the corresponding annotations per
# image describing the regions labeled in the image
source = api.create_source("data/images/cats.zip")
# waiting for the source to be finished. Results will be stored in `source`
api.ok(source)
# step 3: creating a dataset from the previously created `source`
dataset = api.create_dataset(source)
# waiting for the dataset to be finished
api.ok(dataset)
# step 5: creating a deepnet
deepnet = api.create_deepnet(dataset)
# waiting for the deepnet to be finished
api.ok(deepnet)
# the new input data to predict for
input_data = "data/images/cats_test/pexels-pixabay-33358.jpg"
# creating a single prediction
prediction = api.create_prediction(deepnet, input_data)

You can also predict locally using the Deepnet class in the deepnet module. A simple example of that is:

from bigml.deepnet import Deepnet
local_deepnet = Deepnet("deepnet/5968ec46983efc21b000001c")
# predicting for some input data
input_data = "data/images/cats_test/pexels-pixabay-33358.jpg"
local_deepnet.predict(input_data)