<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>

CC BY-SA 4.0
2020-03-01T14:13:26.043Z
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.
mirror2image
CC BY-SA 4.0
2020-03-07T09:58:59.293Z
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
Alexander Soare
<convolutional-neural-networks><object-detection>
How to adapt MTCNN to large images with relatively small ROIs
Alexander Soare
# 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  

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## 0 answers

