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!
Install and update using pip:
pip install coach-ml
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')
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
False to override this behaviour.
model_type can be one of:
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
get_model_remote(model_name, path='.') -> CoachModel
Downloads and loads model into memory. Specify the path of the cached models directory. Returns a
__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
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