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
File size: 679 Bytes
8999949 a327b90 8999949 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | [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"]
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