This session was recorded at ODSC West, 2020, and present by Joseph Nelson

Computer vision unlocks powerful use cases: from models that can identify skin cancer more successfully than doctors (https://news.stanford.edu/2017/01/25/artificial-intelligence-used-identify-skin-cancer/) to tools that identify weeds and reduce pesticide use by 90 percent. However, adoption of computer vision applications has been slow as developers face problems adapting existing state-of-the-art architecture to their own problems. (One repository on Mask_RCNN has 198 open issues mentioning training on one’s “own dataset” alone!)

In this tutorial, we will introduce how to build an object detection model. Specifically, we will build an object detection model that identifies chess pieces (a custom dataset provided by the presenter). In doing so, participants will gain insight into the fundamentals of computer vision: structuring a good problem for object detection, dataset collection and annotation, data preparation through preprocessing, data augmentation to support a well-fit model, training a model, debugging a model’s fit, and using the model for inference. 

Image credit to roboflow


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Training Overview

  • 1

    Applied Deep Learning: Building a Chess Object Detection Model with PyTorch

    • Workshop Overview and Author Bio

    • Getting Started

    • Applied Deep Learning: Building a Chess Object Detection Model with PyTorch

    • Workshop Presention Slides

Instructor Bio:

Joseph Nelson

Cofounder, Principal Data Scientist & Faculty | Roboflow.ai, BetaVector, General Assembly

Joseph Nelson

Joseph Nelson is co-founder and CEO of Roboflow, a computer vision developer tool. Roboflow enables anyone to build better computer vision models quickly. Joseph previously co-founded Represently (acq. 2018). He has been named Distinguished Faculty at General Assembly and worked at companies big (Facebook) and small (failed startups). Joseph is a managing partner at BetaVector, a data science consultancy he co-founded.