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README.md

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+
+---
+tags:
+- image-classification
+- medical-imaging
+- brain-tumor
+- resnet
+- pytorch
+license: apache-2.0
+datasets:
+- sartajbhuvaji/brain-tumor-classification-mri
+metrics:
+- accuracy
+model-index:
+- name: brain-tumor-resnet-classifier
+  results:
+  - task:
+      type: image-classification
+    dataset:
+      name: Brain Tumor Classification MRI
+      type: sartajbhuvaji/brain-tumor-classification-mri
+    metrics:
+    - type: accuracy
+      value: 79.95
+---
+
+# Brain Tumor Classification with ResNet
+
+## Model Description
+
+Bu model, beyin MRI görüntülerinden tümör sınıflandırması yapmak için eğitilmiş bir ResNet modelidir.
+
+## Dataset
+
+Model, [Brain Tumor Classification MRI](https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri) veri seti üzerinde eğitilmiştir.
+
+Veri seti 4 sınıf içermektedir:
+- Glioma
+- Meningioma 
+- No Tumor
+- Pituitary
+
+## Training Details
+
+**En İyi Model Konfigürasyonu:**
+- Model: resnet101
+- Test Accuracy: **79.95%**
+- Validation Accuracy: 97.56%
+
+## Denenen Konfigürasyonlar ve Sonuçları
+
+| Model | Augmentation | Optimizer | Test Acc |
+|-------|-------------|-----------|----------|
+| resnet50 | heavy | - | 76.65% |
+| resnet101 | best | - | 79.95% |
+
+
+## Kullanım
+```python
+import torch
+from torchvision import transforms, models
+from PIL import Image
+
+# Model yükleme
+model = models.resnet101(pretrained=False)
+num_features = model.fc.in_features
+model.fc = torch.nn.Linear(num_features, 4)
+
+checkpoint = torch.hub.load_state_dict_from_url(
+    'https://huggingface.co/Yasette/brain-tumor-resnet-classifier/resolve/main/pytorch_model.pth',
+    map_location='cpu'
+)
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+
+# Görüntü işleme
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Tahmin
+image = Image.open('brain_mri.jpg').convert('RGB')
+input_tensor = transform(image).unsqueeze(0)
+
+with torch.no_grad():
+    output = model(input_tensor)
+    _, predicted = torch.max(output, 1)
+    
+classes = ['glioma', 'meningioma', 'notumor', 'pituitary']
+print(f"Tahmin: {classes[predicted.item()]}")
+```
+
+## Ekip Üyeleri
+
+- [İsim 1]
+- [İsim 2]
+- [İsim 3]
+
+## Lisans
+
+Apache 2.0