Instructions to use amcm/brain-tumor-yolo11n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use amcm/brain-tumor-yolo11n with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("amcm/brain-tumor-yolo11n") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Brain Tumor YOLO11n
Fine-tuned YOLO11n model for brain tumor detection using MRI images.
Intended use
This model is provided for exploration purposes only.
Model
- Architecture: YOLO11n
- Task: Object detection
- Fine-tuned checkpoint:
brain_tumor_yolo11n_ft.pt - Base model: Ultralytics YOLO11n
Training dataset
The model was fine-tuned using the Brain Tumor YOLO dataset from Kaggle.
- Source: Kaggle
- Dataset: Brain Tumor YOLO Dataset
- Purpose: Brain tumor detection and segmentation
- Classes:
- meningioma
- glioma
- pituitary tumor
- Dataset license: Apache License 2.0
The dataset itself is not redistributed with this model. Please refer to the original Kaggle page for the dataset and its licensing terms.
Training
The model was fine-tuned using the prepared dataset split and the YOLO11n base checkpoint.
Limitations
This model is provided for exploration purposes only. Performance may vary depending on image acquisition, preprocessing, dataset distribution, and other factors.
License
The training dataset is licensed under Apache License 2.0. The model is based on Ultralytics YOLO11. YOLO11 models are provided by Ultralytics under AGPL-3.0 and Enterprise licensing options.
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