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README.md

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+---
+license: apache-2.0
+base_model: convnext_tiny_in22k
+tags:
+- medical
+- healthcare
+- image-classification
+- brain-tumor-detection
+datasets:
+- medical-images
+language:
+- en
+library_name: transformers
+pipeline_tag: image-classification
+---
+
+# Brain Tumor Detection
+
+## Model Description
+
+
+    This model is a ConvNeXt Tiny architecture trained with FastAI for detecting brain tumors in MRI scans.
+    It can classify brain MRI images as either showing signs of a tumor or being normal (no tumor detected).
+    Note: This model uses FastAI format and requires specific loading procedures.
+    
+
+## Intended Uses & Limitations
+
+⚠️ **Important**: This model is for research and educational purposes only. It should **NOT** be used for actual medical diagnosis without proper clinical validation and oversight by qualified medical professionals.
+
+### Intended Uses
+- Research and development in medical AI
+- Educational purposes for learning about medical image classification
+- Proof-of-concept applications with proper disclaimers
+- Academic studies and benchmarking
+
+### Limitations
+- Not clinically validated
+- Should not replace professional medical diagnosis
+- May have biases based on training data
+- Performance may vary on different populations or imaging conditions
+
+## Model Details
+
+- **Model Type**: Image Classification
+- **Architecture**: convnext_tiny_in22k
+- **Classes**: 2
+- **Input**: RGB images (224x224 pixels)
+
+
+### Classes
+- No Tumor
+- Tumor Detected
+
+## Usage
+
+```python
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+from PIL import Image
+import torch
+
+# Load model and processor
+model = AutoModelForImageClassification.from_pretrained("your-username/brain-tumor-detection")
+processor = AutoImageProcessor.from_pretrained("your-username/brain-tumor-detection")
+
+# Load and process image
+image = Image.open("path_to_image.jpg")
+inputs = processor(image, return_tensors="pt")
+
+# Make prediction
+with torch.no_grad():
+    outputs = model(**inputs)
+    predicted_class_id = outputs.logits.argmax().item()
+    predicted_class = model.config.id2label[predicted_class_id]
+
+print(f"Predicted class: {predicted_class}")
+```
+
+## Training Details
+
+This model was fine-tuned from pre-trained vision transformers on medical image datasets. For detailed training information, please refer to the original model documentation.
+
+## Evaluation
+
+The model has been tested on held-out validation sets with the reported accuracy metrics. However, clinical evaluation and validation are required before any medical application.
+
+## Ethical Considerations
+
+- Medical AI models can have significant impact on human health
+- Proper validation and regulatory approval required for clinical use  
+- Potential for bias in training data and model predictions
+- Should be used responsibly with appropriate medical oversight
+
+## Contact
+
+For questions about this model, please create an issue in the repository.
+
+## Citation
+
+If you use this model in your research, please cite appropriately and acknowledge that it's for research purposes only.
+
+
+## FastAI Usage
+
+This model uses FastAI format. To use it:
+
+```python
+from fastai.vision.all import load_learner
+import pathlib
+import platform
+
+# Fix for cross-platform compatibility
+if platform.system() == 'Windows':
+    pathlib.PosixPath = pathlib.WindowsPath
+
+# Load the model
+model = load_learner('model.pkl')
+
+# Make prediction
+prediction, pred_idx, probs = model.predict(image)
+print(f"Prediction: {prediction}")
+```
+
+## Requirements
+
+- fastai<2.8.0
+- torch<2.7
+- timm
+- pathlib (for cross-platform compatibility)

model.pkl

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+oid sha256:d80056e02abd309d7d2d0b9ba2899d3305b49465e1baadeb285f9affadebe101
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requirements.txt

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+fastai<2.8.0
+torch<2.7
+torchvision
+timm
+pillow
+numpy