Object Detection with Transformers

Object Detection with Transformers: From Training to Deployment with Determined AI and MLflow

Object detection is a central problem in computer vision and underpins many applications from medical image analysis to autonomous driving. In this talk, we will review the basics of object detection from fundamental concepts to practical techniques. Then, we will dive into cutting-edge methods that use transformers to drastically simplify the object detection pipeline while maintaining predictive performance. Finally, we will show how to train these models at scale using Determined’s integrated deep learning platform and then serve the models using MLflow.

What you will learn:

Basics of object detection including main concepts and techniques
Main ideas from the DETR and Deformable DETR approaches to object detection
Overview of the core capabilities of Determined’s deep learning platform, with a focus on its support for effortless distributed training
How to serve models trained in Determined using MLflow

Connect with us:

[dreamphoenix]

This was beautifully well laid out and to the point. Thank you.

[shombeetdasgupta]

excellent presentation and demo! Is there a link to the github code?

[QuintinMassey]

I’m curious on the follow up video covering object detection with Transformers on small objects. Anyone find that video?

[loucifhebbache]

Hello, thanks for the presentation, can you please share the notebook colab ?

[caiyu]

I compared yolo and detr in google colab. Yolo is much faster than detr and it is a little more precise than detr based on the limited datasets.