__init__(self): pass def onPreprocessing(self, labels, images, workspace_path, params): pass def onTraining(self, monitor_handler): pass def onCompleted(self, model_path): pass def onFailed(self, train_status, e, tb_message): pass • All the logic is kept in a single class • Tensorflow is available by default, with a GPU support • It is possible to perform a preprocessing of the data, e.g. resize the images • Train/test loss and accuracy tracking done simple and exposed to UI
__init__(self): pass def onModelLoading(self, model_path, labels, workspace_path): pass def onTest(self): pass def onInference(self, image_url, params): pass def onFailed(self, deploy_status, e, tb_message): pass • Deployment preparation done as a separate class • Trained models are loaded and then used for the classification
Two types of training: image classification and object detection • A possibility to adapt model hyperparameters • Launched on GPUs, in exactly same way like built-in pretrained models
hyperparameters • A chart of loss vs iteration • Easy one-click deployment of the trained model • Possibility to export the model to .zip file and to use it outside the platform (e.g. mobile device)
ML training for image classification and object detection • Dataset management and labelling integrated in the tool • A deep knowledge of Machine Learning is not necessary • It is possible to extend the platform by providing some custom models, what makes it great for transfer learning, for instance • If you are interested in hearing some more technical details, there is a workshop at Rise of AI tomorrow, 16th March, 4:00pm