Welcome to the August 2020 BASELINE, Novetta’s Machine Learning Newsletter, where we share thoughts on important advances in machine learning technologies likely to impact our customers. This month we cover the following topics: A faster implementation for object detection Better explainability tools for text models Advances in language models and related tasks PP-YOLO: An Effective and Efficient Object Detection Implementation PP-YOLO is a recently-released object detector that advances the state-of-the-art in object detector models, achieving faster, more accurate results on the COCO dataset than predecessors such as EfficientDet and YOLOv4. Striking a balance between accuracy and inference speed, PP-YOLO is…
ADSynth Blog Series: AWS Pipeline and iOS App Creation
Part 4 of 4 - See a demo at Strata Data Conference Booth 1201!
Matthew and Jack interned with Novetta’s Machine Learning Center of Excellence during the summer of 2019. This blog series discusses ADSynth, an app that creates a digital architecture diagram from a photo of a whiteboard sketch. As introduced in our first blog post, we built a simple iOS application alongside an AWS pipeline to provide an end-to-end solution to diagram creation. AWS was the obvious solution to build a truly scalable system capable of supporting multiple users creating diagrams at once. We were already using GPU enabled EC2 instances during the development of our object detection model, hence most…
ADSynth Blog Series: Defining the AWS Architecture Diagram
Part 3 of 4
Matthew and Jack interned with Novetta’s Machine Learning Center of Excellence during the summer of 2019. This blog series discusses ADSynth, an app that creates a digital architecture diagram from a photo of a whiteboard sketch. ADSynth leverages numerous image processing techniques to properly translate a whiteboard sketch into an editable, digital architecture diagram. These techniques are used to ensure that all detected components are connected and formatted correctly. This post describes how we utilized our YOLOv3 object detection model predictions in conjunction with the image of the whiteboard sketch to obtain a clean, presentable graphic ready for insertion…
ADSynth Blog Series: Object Detection Model and Training Data
Part 2 of 4
Matthew and Jack interned with Novetta’s Machine Learning Center of Excellence during the summer of 2019. This blog series discusses ADSynth, an app that creates a digital architecture diagram from a photo of a whiteboard sketch. We began with the backbone of ADSynth: the object detection model that makes diagram synthesis possible, as well as the data necessary to train the model. Implementation of YOLOv3 Architecture Based on the research we conducted on object detection, the architecture we decided to implement was YOLOv3. YOLOv3 is a deep neural network comprising of 106 layers and almost 63 million parameters. The…
ADSynth Blog Series: Digitizing Whiteboard Diagrams
Part 1 of 4
Matthew and Jack interned with Novetta’s Machine Learning Center of Excellence during the summer of 2019. This blog series discusses ADSynth, an app that creates a digital architecture diagram from a photo of a whiteboard sketch. This summer, we interned with Novetta’s Machine Learning Center of Excellence (ML COE). As computer science students with a passion for machine learning and problem solving, we were excited to have the opportunity to apply our classroom knowledge in an industry setting. The experience was rewarding, though transferring our theoretical understanding of machine learning to practice presented a steep learning curve (pun intended)….
