<p>It depends, if astronomers are just identifying which star, which arm, using preexisting datasets like GalaxyZoo, they use preexisting neural networks. But for more complex tasks such as find the spectroscopic specification, the rotational velocity via the Tully fisher relation, they use custom neural networks or just make some tweaks but still feed them on preexisting datasets</p>
<p>But I guess that they either would do some tweaks in preexisting neural networks, or create their own (My best guess) because things like YOLO, for image identification don't have many feature specified for astronomy like taking care of noise to signal ration, spectral lines, etc.. which is important for complex study of  morphology of galaxies, but ameature astronomers who aim to only find which type of galaxy and basic details is can use things like YOLO with proper datasets like GalaxyZoo.</p>
<p>Actually they made a neural network (<a href="https://academic.oup.com/mnras/article/511/4/5032/6529251" rel="nofollow noreferrer">by XAI using saliency maps</a>), to find the morphology of galaxies using saliency maps.</p>
<p>Thanks!</p>

CC BY-SA 4.0
2023-08-20T06:25:02.333Z
2023-08-20T06:34:00.760Z
2023-08-20T06:34:00.760Z
Arjun
<p>When a researcher first decides to use AI to solve a basic classification problem, such as training a neural network to determine the morphology of a galaxy, are they more likely to start by using preexisting models, such as <a href="https://www.v7labs.com/blog/yolo-object-detection" rel="nofollow noreferrer">YOLO</a> or <a href="https://detectron2.readthedocs.io/en/latest/" rel="nofollow noreferrer">detectron2</a> and train these on a custom dataset, or to create a new neural network from the ground up, with something like <a href="https://keras.io/" rel="nofollow noreferrer">keras</a>?</p>

CC BY-SA 4.0
2023-08-19T22:11:09.420Z
*Welcome to Stack Exchange!* I made a small edit to the wording to align the text with the title and switch from "What should I do?" to "What do researchers usually do?" to avoid primarily opinion-based answers. If you can add more information like roughly what computing power is available and how long one would expect results (3 weeks? 3 months? 3 years?)
uhoh
CC BY-SA 4.0
2023-08-20T05:08:23.050Z
I hope this Journal would be helpful: https://academic.oup.com/mnras/article/511/4/5032/6529251
Arjun
<galaxy><neural-networks>
Do researchers create their own convolutional neural network models for their work or do they use existing ones (e.g. YOLO, detectron2, etc)
Chris K.
# Do researchers create their own convolutional neural network models for their work or do they use existing ones (e.g. YOLO, detectron2, etc)

(Asked by: username_0 on 2023-08-19)

When a researcher first decides to use AI to solve a basic classification problem, such as training a neural network to determine the morphology of a galaxy, are they more likely to start by using preexisting models, such as [YOLO](https://www.v7labs.com/blog/yolo-object-detection) or [detectron2](https://detectron2.readthedocs.io/en/latest/) and train these on a custom dataset, or to create a new neural network from the ground up, with something like [keras](https://keras.io/)?

**Tags:** galaxy, neural-networks

> **Comments:**
> **username_1**: *Welcome to Stack Exchange!* I made a small edit to the wording to align the text with the title and switch from "What should I do?" to "What do researchers usually do?" to avoid primarily opinion-based answers. If you can add more information like roughly what computing power is available and how long one would expect results (3 weeks? 3 months? 3 years?)  
> **username_2**: I hope this Journal would be helpful: https://academic.oup.com/mnras/article/511/4/5032/6529251  

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## 1 answers

### Answer by username_2 on 2023-08-20. Score: 1 | Accepted answer

It depends, if astronomers are just identifying which star, which arm, using preexisting datasets like GalaxyZoo, they use preexisting neural networks. But for more complex tasks such as find the spectroscopic specification, the rotational velocity via the Tully fisher relation, they use custom neural networks or just make some tweaks but still feed them on preexisting datasets

But I guess that they either would do some tweaks in preexisting neural networks, or create their own (My best guess) because things like YOLO, for image identification don't have many feature specified for astronomy like taking care of noise to signal ration, spectral lines, etc.. which is important for complex study of morphology of galaxies, but ameature astronomers who aim to only find which type of galaxy and basic details is can use things like YOLO with proper datasets like GalaxyZoo.

Actually they made a neural network ([by XAI using saliency maps](https://academic.oup.com/mnras/article/511/4/5032/6529251)), to find the morphology of galaxies using saliency maps.

Thanks!


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