Nyuntam Vision

Overview

Developed to compress and optimize deep learning models, Nyuntam Vision provides a set of compression techniques tailored for specific deployment constraints. Users have the flexibility to choose and combine multiple techniques to achieve the best trade-off between model performance and deployment constraints. Leveraging cutting-edge techniques like pruning, quantization, distillation, etc. It achieves exceptional model compression levels on a variety of vision models.

Basic Workflow

Step 1 - Import a model and dataset

Check Import Data and Import Model for the exact steps. Make sure that the model-dataset combination provided is valid and of the same task.

Step 2 - Choose an Algorithm

Check available Algorithms and respective hyperparameters. By default, most optimal hyperparameters are chosen, however, depending upon the task, model and dataset another set of hyperparameters can work better too.

Step 3 - Monitor Logs and Export

By default, Job logs and model checkpoints are saved in nyuntam-vision/user_data/, however, users can specify the folder then want to store the logs in by changing the LOGGING_PATH argument in the yaml.