Body: <p>This question could be generalised to how to adapt state-of-the-art object detection models to large images with small ROIs.</p>
  
  <p>In my particular case I'm trying to use <a href="https://github.com/ipazc/mtcnn" rel="nofollow noreferrer">this implementation</a> of <a href="https://arxiv.org/pdf/1604.02878.pdf" rel="nofollow noreferrer">MTCNN</a> to get bounding boxes for the faces of images of statues.</p>
  
  <p>One challenge is that the face could take up a large proportion of the image like this:</p>
  
  <p><a href="https://i.sstatic.net/Fg1jM.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Fg1jM.jpg" alt="enter image description here"></a></p>
  
  <p>Or a very small proportion of the image like this:</p>
  
  <p><a href="https://i.sstatic.net/PwsZR.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/PwsZR.jpg" alt="enter image description here"></a></p>
  
  <p>Where I'll zoom in on the statue's face so you can see it:</p>
  
  <p><a href="https://i.sstatic.net/lXUbs.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/lXUbs.png" alt="enter image description here"></a></p>
  
  <p><strong>Bonus</strong></p>
  
  <p>If anyone has additional comments on my overall approach to this particular problem, happy to hear them.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-01T14:13:26.043Z
Comments.Text: The paper approach use image pyramid with downscaling only. That create problem for big images with small faces. You may be able bypass it with brute force sliding window approach: 1. Train on upscaled low-res images 2. for big images apply detector in sliding subwindow on several scales, with subwindow upscaled to detector resolution.
Comments.UserDisplayName: mirror2image
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-03-07T09:58:59.293Z
Comments.Text: Thanks. I decided to ditch MTCNN for a while in favor of an approach of getting the statue with Faster R-CNN, then we'll see where I go from there
Comments.UserDisplayName: Alexander Soare
Tags: <convolutional-neural-networks><object-detection>
Title: How to adapt MTCNN to large images with relatively small ROIs
UserDisplayName: Alexander Soare
ThreadText: # How to adapt MTCNN to large images with relatively small ROIs
  
  (Asked by: username_0 on 2020-02-29)
  
  This question could be generalised to how to adapt state-of-the-art object detection models to large images with small ROIs.
  
  In my particular case I'm trying to use [this implementation](https://github.com/ipazc/mtcnn) of [MTCNN](https://arxiv.org/pdf/1604.02878.pdf) to get bounding boxes for the faces of images of statues.
  
  One challenge is that the face could take up a large proportion of the image like this:
  
  [![enter image description here](https://i.sstatic.net/Fg1jM.jpg)](https://i.sstatic.net/Fg1jM.jpg)
  
  Or a very small proportion of the image like this:
  
  [![enter image description here](https://i.sstatic.net/PwsZR.jpg)](https://i.sstatic.net/PwsZR.jpg)
  
  Where I'll zoom in on the statue's face so you can see it:
  
  [![enter image description here](https://i.sstatic.net/lXUbs.png)](https://i.sstatic.net/lXUbs.png)
  
  **Bonus**
  
  If anyone has additional comments on my overall approach to this particular problem, happy to hear them.
  
  **Tags:** convolutional-neural-networks, object-detection
  > **Comments:**
  > **username_1**: The paper approach use image pyramid with downscaling only. That create problem for big images with small faces. You may be able bypass it with brute force sliding window approach: 1. Train on upscaled low-res images 2. for big images apply detector in sliding subwindow on several scales, with subwindow upscaled to detector resolution.  
  > **username_0**: Thanks. I decided to ditch MTCNN for a while in favor of an approach of getting the statue with Faster R-CNN, then we'll see where I go from there  
  
  ---
  
  ## 0 answers
  
  