Image Classification
Transformers
Safetensors
Flair
vit
medical-imaging
brain-tumor
mri
vision-transformer
Instructions to use Songline/BrainTumor_FlairClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Songline/BrainTumor_FlairClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Songline/BrainTumor_FlairClassifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Songline/BrainTumor_FlairClassifier") model = AutoModelForImageClassification.from_pretrained("Songline/BrainTumor_FlairClassifier", device_map="auto") - Flair
How to use Songline/BrainTumor_FlairClassifier with Flair:
from flair.models import SequenceTagger tagger = SequenceTagger.load("Songline/BrainTumor_FlairClassifier") - Notebooks
- Google Colab
- Kaggle
| [build-system] | |
| requires = ["setuptools>=68"] | |
| build-backend = "setuptools.build_meta" | |
| [project] | |
| name = "brain-tumor-flair-classifier" | |
| version = "0.1.0" | |
| description = "FLAIR NIfTI brain tumor binary classifier" | |
| readme = "README.md" | |
| requires-python = ">=3.10" | |
| license = { file = "LICENSE" } | |
| authors = [ | |
| { name = "Songline-music" }, | |
| ] | |
| dependencies = [ | |
| "hf-xet>=1.1", | |
| "huggingface-hub>=0.27,<1.0", | |
| "nibabel>=5.0,<6.0", | |
| "numpy>=2.0,<3.0", | |
| "Pillow>=10.0,<13.0", | |
| "safetensors>=0.5,<1.0", | |
| "torch>=2.5", | |
| "transformers>=4.53,<5.0", | |
| ] | |
| [project.scripts] | |
| brain-tumor-flair-classify = "brain_tumor_flair_classifier.cli:main" | |
| [tool.setuptools.packages.find] | |
| where = ["src"] | |