<p>I don't think the BirdNET GUI contains anything for &quot;correcting&quot; or <em><strong>re-training</strong></em> the classifier.</p>
<p>However, using the <strong>BirdNET command-line version</strong> you can <a href="https://github.com/kahst/BirdNET-Analyzer#training" rel="nofollow noreferrer">train the algorithm yourself - instructions here</a> - and this is what you want to do. In this context, &quot;correcting&quot; the algorithm would be re-training it with the original training dataset (ideally) PLUS the examples that you've manually labelled that the algorithm was getting wrong.</p>
<p>The &quot;original training dataset&quot; might be unavailable or too big for you to use for re-training. Perhaps try making a not-too-small generic dataset of all the species you're interested in, plus the manual corrections.</p>

CC BY-SA 4.0
2024-01-02T17:09:38.590Z
2024-01-02T17:09:38.590Z
Dan Stowell
<p>I agree with Dan, I'll just mention another possible direction:</p>
<p>An iterative training workflow, known as &quot;active learning&quot; or &quot;human-in-the-loop&quot;, takes a bit more work to set up than just training a custom classifier on top of BirdNET. Researchers at Google have provided some tutorials and notebooks for active learning (they also call it &quot;agile modeling&quot;) relying on their pre-trained model Perch, rather than BirdNET:
<a href="https://colab.research.google.com/drive/1gPBu2fyw6aoT-zxXFk15I2GObfMRNHUq?usp=sharing" rel="nofollow noreferrer">https://colab.research.google.com/drive/1gPBu2fyw6aoT-zxXFk15I2GObfMRNHUq?usp=sharing</a></p>
<p>It will require more hands-on programming than the BirdNET interface.</p>

CC BY-SA 4.0
2024-01-26T21:19:12.707Z
2024-01-26T21:19:12.707Z
Sam Lapp
<p>I have been doing the same over te past months.
Generating a classifier with training data and then regenerating a new classifier with the false positive data of the first classifier. I just add them to a folder &quot;Noise&quot; in the folder of the training data and then train a new classifier. This has slowly been working, but is an incredible iterative process that takes a lot of time for me unfortunately.</p>

CC BY-SA 4.0
2024-09-04T11:18:16.557Z
2024-09-05T15:45:08.193Z
2024-09-05T15:45:08.193Z
Tjomme Fernhout
<p>I am using BirdNET GUI for analysis of bird vocalizations. I have created a classifier using the GUI and some training data, and I am wanting to improve it, e.g. correct false classifications and allow the classifier to learn from this. Does anyone know of a workflow for this, either within the GUI or using RavenPro or other audio analysis software? I know it is a relatively new platform, and I haven't been able to find reference materials about this process.</p>

<analysis><machine-learning><birdnet><classifier>
BirdNET: workflow for correcting manually trained classifier?
# BirdNET: workflow for correcting manually trained classifier?

(Asked by: username_0 on 2023-12-14)

I am using BirdNET GUI for analysis of bird vocalizations. I have created a classifier using the GUI and some training data, and I am wanting to improve it, e.g. correct false classifications and allow the classifier to learn from this. Does anyone know of a workflow for this, either within the GUI or using RavenPro or other audio analysis software? I know it is a relatively new platform, and I haven't been able to find reference materials about this process.

**Tags:** analysis, machine-learning, birdnet, classifier


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## 3 answers

### Answer by username_1 on 2024-01-02. Score: 3

I don't think the BirdNET GUI contains anything for "correcting" or ***re-training*** the classifier.

However, using the **BirdNET command-line version** you can [train the algorithm yourself - instructions here](https://github.com/kahst/BirdNET-Analyzer#training) - and this is what you want to do. In this context, "correcting" the algorithm would be re-training it with the original training dataset (ideally) PLUS the examples that you've manually labelled that the algorithm was getting wrong.

The "original training dataset" might be unavailable or too big for you to use for re-training. Perhaps try making a not-too-small generic dataset of all the species you're interested in, plus the manual corrections.


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### Answer by username_2 on 2024-01-26. Score: 2

I agree with Dan, I'll just mention another possible direction:

An iterative training workflow, known as "active learning" or "human-in-the-loop", takes a bit more work to set up than just training a custom classifier on top of BirdNET. Researchers at Google have provided some tutorials and notebooks for active learning (they also call it "agile modeling") relying on their pre-trained model Perch, rather than BirdNET:
<https://colab.research.google.com/drive/1gPBu2fyw6aoT-zxXFk15I2GObfMRNHUq?usp=sharing>

It will require more hands-on programming than the BirdNET interface.


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### Answer by username_3 on 2024-09-04. Score: 1

I have been doing the same over te past months.
Generating a classifier with training data and then regenerating a new classifier with the false positive data of the first classifier. I just add them to a folder "Noise" in the folder of the training data and then train a new classifier. This has slowly been working, but is an incredible iterative process that takes a lot of time for me unfortunately.


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