Image Classification
Transformers
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use ShimaGh/Brain-Tumor-Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShimaGh/Brain-Tumor-Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ShimaGh/Brain-Tumor-Detection") 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("ShimaGh/Brain-Tumor-Detection") model = AutoModelForImageClassification.from_pretrained("ShimaGh/Brain-Tumor-Detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b427df7cee0df98c0adbe651f59da8aae57bdc9180da53b436e598cebf0487e3
- Size of remote file:
- 4.6 kB
- SHA256:
- 159dc14a870c6a6d08315eafbebbb7b5a6c905a5fadc65a17315283edd6ff9be
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