# About Yolo Configuration File (YAML)

**URL:** <https://community.ultralytics.com/t/about-yolo-configuration-file-yaml/300>\
**Category:** Discussion\
**Tags:** discussion\
**Created:** [September 29, 2024, 5:10pm UTC](https://community.ultralytics.com/t/about-yolo-configuration-file-yaml/300 "2024-09-29T17:10:53Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![binoddeka](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/binoddeka/32/297_2.png) [@binoddeka](https://community.ultralytics.com/u/binoddeka)\
**Post date:** [September 29, 2024, 5:10pm UTC](https://community.ultralytics.com/t/about-yolo-configuration-file-yaml/300/1 "2024-09-29T17:10:53Z")

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I am using YOLO-8 for training rice disease detection. The configuration of yaml files are:

```yaml
  train: /content/drive/MyDrive/ResearchWork/DataSet/YOLO dataset/Set-3/train/images
  val: /content/drive/MyDrive/ResearchWork/DataSet/YOLO dataset/Set-3/valid/images
  test: /content/drive/MyDrive/ResearchWork/DataSet/YOLO dataset/Set-3/test/images
  nc: 11
  names: ['Bacterial blight', 'Bacterial leaf', 'Brown spot', 'Cuterpillar', 'Drainage impact', 'Grashopper damage', 'Grassy stunt', 'Leaf folder', 'Sheath blight', 'Stem borer', 'Tungro']

```

**Can I skip few class (like Tungro,Brown spot etc) during training testing & valdations without changing dataset or annonations.**

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<div class="post-metadata">

**Author:** ![pderrenger](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/pderrenger/32/73_2.png) [@pderrenger](https://community.ultralytics.com/u/pderrenger)\
**Post date:** [September 30, 2024, 12:36am UTC](https://community.ultralytics.com/t/about-yolo-configuration-file-yaml/300/2 "2024-09-30T00:36:20Z")

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Hello! 😊

Yes, you can skip certain classes during training, validation, and testing without altering your dataset or annotations. You can achieve this by using the `classes` argument in your training script to specify which classes you want to include. Here’s a quick example:

```python
from ultralytics import YOLO

# Load your model
model = YOLO('yolov8n.pt')

# Train the model, specifying the classes you want to include
model.train(data='your_data.yaml', classes=[0, 1, 3, 4, 5, 6, 7, 8, 9]) # Excludes 'Tungro' and 'Brown spot'

```

In this example, replace the indices in the `classes` list with those corresponding to the classes you want to include. The indices should match the order in your `names` list.

For more details, you can check out the [Ultralytics documentation](https://docs.ultralytics.com).

If you have any more questions, feel free to ask! 🚀

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<div class="post-metadata">

**Author:** ![binoddeka](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/binoddeka/32/297_2.png) [@binoddeka](https://community.ultralytics.com/u/binoddeka)\
**Post date:** [September 30, 2024, 4:23am UTC](https://community.ultralytics.com/t/about-yolo-configuration-file-yaml/300/3 "2024-09-30T04:23:15Z")

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Thanks for the quick response.