Hey there! 😊 The easiest way is to train with data=brain-tumor.yaml, which downloads the dataset automatically the first time. To download it manually, use:

wget https://github.com/ultralytics/assets/releases/download/v0.0.0/brain-tumor.zip
unzip brain-tumor.zip -d path/to/destination

Replace path/to/destination with the directory where you'd like the dataset stored. Once you have the dataset, you're all set to start training or inferencing with YOLO models on brain tumor images!

Should you need further guidance, feel free to ask. Happy coding! 🚀

The packaged brain-tumor zip only includes image-level labels with anonymized filenames (no patient IDs/metadata), so we can’t provide “complete identifiers” or guarantee a patient-disjoint re-split; for patient-wise splitting you’ll need to rebuild from a source that includes patient IDs (e.g., your DICOM/NIfTI originals) and then generate train.txt/val.txt, and if you just need stronger evaluation without IDs you can use our K-Fold Cross Validation guide—please keep follow-ups here in the GitHub discussion (we can’t provide support over email/DM).

The packaged brain-tumor dataset split is provided at the image-folder level and doesn’t ship patient IDs/metadata, so we can’t guarantee a patient-disjoint (no same patient in both train/val) split; if you need patient-wise separation, you’ll need to rebuild the split from patient identifiers and point a custom YAML to *.txt image lists (supported by Ultralytics YOLO), e.g. see the provided config in the Brain Tumor dataset docs and the brain-tumor.yaml in the repo.

# my-brain-tumor.yaml
path: /data/brain-tumor
train: splits/train.txt  # one image path per line (ideally grouped by patient ID when you create it)
val: splits/val.txt
names:
  0: negative
  1: positive

@xySun17 hello!

To train YOLO26 without loading the pre-trained weights, you can initialize the model from scratch by specifying the model architecture file (e.g., yolo26n.yaml) instead of the pre-trained weights file. Here’s how you can do it:

Python

from ultralytics import YOLO

# Load a model architecture without pre-trained weights
model = YOLO("yolo26n.yaml")  # build a new model from YAML

# Train the model
results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)

CLI

# Train from scratch using the model architecture file
yolo detect train data=brain-tumor.yaml model=yolo26n.yaml epochs=100 imgsz=640

This approach ensures that the model starts training with random weights rather than pre-trained ones. If you encounter any issues, please ensure you are using the latest version of the Ultralytics package and provide a minimum reproducible example if the problem persists. You can find more details on creating a reproducible example here.

Feel free to reach out if you have any further questions. Happy training! 🚀

Thank you!! However, there is another problem, that is, why it says the path of dataset is not found? I already downloaded the dataset files and changed the path in "brain-tumor.yaml" file.

The threw error was follows: "Dataset 'brain-tumor.yaml' images not found ⚠️, missing path '/valid/images' Note dataset download directory is '/home/ros/datasets'. You can update this in '/home/ros/.config/Ultralytics/settings.yaml'"

Hello @xySun17,

Thank you for reaching out! It looks like the issue might be related to the dataset path configuration in your brain-tumor.yaml file. Here are a few steps to troubleshoot and resolve this:

1. Verify Dataset Path: Ensure that the paths specified in your brain-tumor.yaml file correctly point to the locations of your dataset images. For example:

   path: /home/ros/datasets/brain-tumor
   train: images/train
   val: images/val

2. Check Directory Structure: Confirm that the directory structure matches what is specified in the YAML file. The images/train and images/val directories should exist within /home/ros/datasets/brain-tumor.

3. Update Configuration: If the dataset is located in a different directory, update the path in the YAML file accordingly. For instance, if your dataset is in /home/ros/my-datasets/brain-tumor, update the path to:

   path: /home/ros/my-datasets/brain-tumor

4. Reproducible Example: If the issue persists, providing a minimum reproducible example can greatly help in diagnosing the problem. You can find guidance on creating a reproducible example here.

5. Latest Version: Ensure you are using the latest version of the Ultralytics package. You can update it using:

   pip install --upgrade ultralytics

Here’s an example of how your brain-tumor.yaml should look:

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Brain-tumor dataset by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/detect/brain-tumor/
# Example usage: yolo train data=brain-tumor.yaml

path: /home/ros/datasets/brain-tumor  # dataset root dir
train: images/train  # train images (relative to 'path')
val: images/val  # val images (relative to 'path')

# Classes
names:
  0: negative
  1: positive

# Download script/URL (optional)
download: https://github.com/ultralytics/assets/releases/download/v0.0.0/brain-tumor.zip

If you follow these steps and still encounter issues, please share more details or any error messages you receive. This will help us assist you better.

Best regards and happy coding! 🚀