Instructions to use prithivMLmods/BrainTumor-Classification-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/BrainTumor-Classification-Mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/BrainTumor-Classification-Mini") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/BrainTumor-Classification-Mini") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/BrainTumor-Classification-Mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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# **BrainTumor-Classification-Mini**
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> **BrainTumor-Classification-Mini** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify brain tumor images using the **SiglipForImageClassification** architecture.
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- **Medical Diagnosis Assistance:** Supporting radiologists in preliminary tumor classification.
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- **AI-Assisted Healthcare:** Enhancing automated tumor detection in medical imaging.
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- **Research & Development:** Facilitating studies in AI-driven medical imaging solutions.
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- **Educational Purposes:** Helping students and professionals learn about tumor classification using AI.
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license: apache-2.0
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# **BrainTumor-Classification-Mini**
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> **BrainTumor-Classification-Mini** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify brain tumor images using the **SiglipForImageClassification** architecture.
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- **Medical Diagnosis Assistance:** Supporting radiologists in preliminary tumor classification.
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- **AI-Assisted Healthcare:** Enhancing automated tumor detection in medical imaging.
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- **Research & Development:** Facilitating studies in AI-driven medical imaging solutions.
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- **Educational Purposes:** Helping students and professionals learn about tumor classification using AI.
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