Deep learning is at the forefront of most machine learning (ML) implementations across a broad set of business verticals. Driven by the highly flexible nature of neural networks, the boundary of what is possible has been pushed to a point where neural networks can outperform humans in a variety of tasks, such as object detection tasks in the context of computer vision (CV) problems.
Object detection, which is one type of CV task, has many applications in various fields like medicine, retail, or agriculture. For example, retail businesses want to be able to detect stock keeping units (SKUs) in store shelf images to analyze buyer trends or identify when product restock is necessary. Object detection models allow you to implement these diverse use cases and automate your in-store operations.
In this post, we discuss Detectron2, an object detection and segmentation framework released by Facebook AI Research (FAIR), and its implementation on Amazon SageMaker to solve a dense object detection task for retail. This post includes an associated sample notebook, which you can run to demonstrate all the features discussed in this post. For more information, see the GitHub repository.
Toolsets used in this solution
To implement this solution, we use Detectron2, PyTorch, SageMaker, and the public SKU-110K dataset.
Detectron2 is a ground-up rewrite of Detectron that started with maskrcnn-benchmark. The platform is now implemented in PyTorch. With a new, more modular design, Detectron2 is flexible and extensible, and provides fast training on single or multiple GPU servers. Detectron2 includes high-quality implementations of state-of-the-art object detection algorithms, including DensePose, panoptic feature pyramid networks, and numerous variants of the pioneering Mask R-CNN model family also developed by FAIR. Its extensible design makes it easy to implement cutting-edge research projects without having to fork the entire code base.
PyTorch is an open-source, deep learning framework that makes it easy to develop ML models and deploy them to production. With PyTorch’s TorchScript, developers can seamlessly transition between eager mode, which performs computations immediately for easy development, and graph mode, which creates computational graphs for efficient implementations in production environments. PyTorch also offers distributed training, deep integration into Python, and a rich ecosystem of tools and libraries, which makes it popular with researchers and engineers.
An example of that rich ecosystem of tools is TorchServe, a recently released model-serving framework for PyTorch that helps deploy trained models at scale without having to write custom code. TorchServe is built and maintained by AWS in collaboration with Faceboo
Source - Continue Reading: https://aws.amazon.com/blogs/machine-learning/object-detection-with-detectron2-on-amazon-sagemaker/