Python client for coach
Project description
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. This SDK is Python 3.6 compatible, we're working on 3.7 compatibility in the future.
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.