coach-ml 0.22

Python client for coach

Coach Python SDK

Coach is an end-to-end Image Recognition platform, we provide the tooling to do effective data collection, training, and on-device parsing of Image Recognition models.

See https://coach.lkuich.com for more information!

Installing

Install and update using pip:

pip install coach-ml

Usage

Coach can be initialized 2 different ways. If you are only using the offline model parsing capabilities and already have a model package on disk, you can initialize like so:

coach = CoachClient()

# We already had the `flowers` model on disk, no need to authenticate:
result = coach.get_model('flowers').predict('rose.jpg')

However, in order to download your trained models, you must authenticate with your API key:

coach = CoachClient().login('myapikey')

# Now that we're authenticated, we can cache our models for future use:
coach.cache_model('flowers')

# Evaluate with our cached model:
result = coach.get_model('flowers').predict('rose.jpg')

API Breakdown

CoachClient

__init__(is_debug=False)
Optional is_debug, if True, additional logs will be displayed

login(apiKey) -> CoachClient
Authenticates with Coach service and allows for model caching. Accepts API Key as its only parameter. Returns its own instance.

cache_model(model_name, path='.', skip_match=False, model_type='frozen') Downloads model from Coach service to disk. Specify the name of the model, and the path to store it. This will create a new directory in the specified path and store any model related documents there. By default, if a model already exists with the same version, in the same path, caching will be skipped. Set skip_match to False to override this behaviour. model_type can be one of: frozen, unity, mobile, and can be useful if you're interested in caching a specific version of your model.

get_model(path='.') -> CoachModel Loads model into memory. Specify the path of the cached models directory. Returns a CoachModel

get_model_remote(model_name, path='.') -> CoachModel Downloads and loads model into memory. Specify the path of the cached models directory. Returns a CoachModel

CoachModel

__init__(graph, labels, base_module)
Initializes a new instance of CoachModel, accepts a loaded tf.Graph(), array of labels, and the base_module the graph was trained off of.

predict(image, input_name="input", output_name="output") -> dict
Specify the directory of an image file or the image as a byte array. Parses the specified image into memory and runs it through the loaded model. Returns a dict of its predictions in order of confidence. If you have a pretrained frozen graph with different Tensor input/output names, you can specify them with input_name and output_name respectfully.