DETR: End-to-End Object Detection with Transformers (Paper Explained)

Object detection in images is a notoriously hard task! Objects can be of a wide variety of classes, can be numerous or absent, they can occlude each other or be out of frame. All of this makes it even more surprising that the architecture in this paper is so simple. Thanks to a clever loss function, a single Transformer stacked on a CNN is enough to handle the entire task!

OUTLINE: 0:00 - Intro & High-Level Overview 0:50 - Problem Formulation 2:30 - Architecture Overview 6:20 - Bipartite Match Loss Function 15:55 - Architecture in Detail 25:00 - Object Queries 31:00 - Transformer Properties 35:40 - Results

ERRATA: When I introduce bounding boxes, I say they consist of x and y, but you also need the width and height.

My Video on Transformers: https://youtu.be/iDulhoQ2pro

Paper: https://arxiv.org/abs/2005.12872 Blog: https://ai.facebook.com/blog/end-to-end-object-detection-with-transformers/ Code: https://github.com/facebookresearch/detr

Abstract: We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive baselines. Training code and pretrained models are available at this https URL.

Authors: Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko

Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher BitChute: https://www.bitchute.com/channel/yannic-kilcher Minds: https://www.minds.com/ykilcher ... https://www.youtube.com/watch?v=T35ba_VXkMY