This repository contains Jupyter Notebook tutorials for computer vision use-cases. The tutorials take you end-to-end through the process of developing a deep-learning model for computer vision:
- Load, Explore, and Understand the data relevant to your computer vision task
- Prototype deep learning models in the MXNet Framework, using both the MXNet Symbolic API and the imperative Gluon interface.
- Port prototype code to run scalable training jobs using the Amazon SageMaker platform
- Deploy trained models to inference endpoints using Amazon SageMaker
- Deploy trained models to the Edge using AWS DeepLens.
- Image Classification: A level-101 Intro to Computer Vision with Deep Learning, this tutorial covers a very simple image classification task for traffic sign classification.
- Transfer Learning
- Object-Detection
- Semantic Segmentation
This library is licensed under the Apache 2.0 License.