Body: <h2>The Context</h2>
  <p>From all of the problems I have worked with in computer vision, the most challenging one is the object detection. This is not because the problem itself is complex to understand or bad formulated. But because we need to inject some strong priors about how we understand the world. Those priors are the <em>anchors</em> (which are priors about object shapes, aspect ratio...).</p>
  <p>This prior information, although very simple to understand, it is very hard to inject on the training logic. Hence making the computation of the ground truth very messy and prone to errors. It is even  harder when different object detection backbones propose different ground truth computation methods.</p>
  <h2>The Question</h2>
  <p>From mid-2019 till now there is a growing trend on research about one-stage object detectors that do not rely on anchors: hence dropping the costly NMS postprocessing and in some cases even the IoU computation. I would like to do a proof of concept with some of them so here is my question:</p>
  <p><em>What are some good object detectors that do not use anchors? Or said in other words, what are the go-to object detectors for this new research trend?</em></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-28T09:36:54.120Z
Comments.Text: i don't believe avoiding anchor points would be a good approach, as humans always focus on a point before identifying what it is
Comments.UserDisplayName: Dan D.
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-28T13:49:05.630Z
Comments.Text: Another approach is, as you say, identify object as points (see paper with the same name "Objects as Points"), I feel the anchors compute is something the network should learn by itself not y us injecting them
Comments.UserDisplayName: JVGD
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-17T05:00:03.040Z
Comments.Text: The network you are looking for is DETR by Facebook.
Comments.UserDisplayName: Abhishek Verma
Tags: <computer-vision><object-detection><model-request>
Title: Object detection approaches without anchors and NMS
UserDisplayName: JVGD
ThreadText: # Object detection approaches without anchors and NMS
  
  (Asked by: username_0 on 2021-01-28)
  
  ## The Context
  
  From all of the problems I have worked with in computer vision, the most challenging one is the object detection. This is not because the problem itself is complex to understand or bad formulated. But because we need to inject some strong priors about how we understand the world. Those priors are the *anchors* (which are priors about object shapes, aspect ratio...).
  
  This prior information, although very simple to understand, it is very hard to inject on the training logic. Hence making the computation of the ground truth very messy and prone to errors. It is even harder when different object detection backbones propose different ground truth computation methods.
  
  ## The Question
  
  From mid-2019 till now there is a growing trend on research about one-stage object detectors that do not rely on anchors: hence dropping the costly NMS postprocessing and in some cases even the IoU computation. I would like to do a proof of concept with some of them so here is my question:
  
  *What are some good object detectors that do not use anchors? Or said in other words, what are the go-to object detectors for this new research trend?*
  
  **Tags:** computer-vision, object-detection, model-request
  > **Comments:**
  > **username_1**: i don't believe avoiding anchor points would be a good approach, as humans always focus on a point before identifying what it is  
  > **username_0**: Another approach is, as you say, identify object as points (see paper with the same name "Objects as Points"), I feel the anchors compute is something the network should learn by itself not y us injecting them  
  > **username_2**: The network you are looking for is DETR by Facebook.  
  
  ---
  
  ## 0 answers
  
  