Create README.md

README.md

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+---
+license: mit
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+pipeline_tag: image-classification
+tags:
+- medical
+- cervical-cancer
+- multi-class
+- ood-detection
+---
+
+# Model Card: DenseNet121 for Cervix type Image Classification
+
+This model classifies cervical images into **Type_1, Type_2, Type_3**, and an **Out-of-Distribution (OOD)** category. It uses a **DenseNet121 backbone** pretrained on ImageNet and fine-tuned on cervical images, including OOD examples from Caltech101.  
+
+
+### Model Details
+
+- **Base model:** `torchvision.models.densenet121` pretrained on ImageNet  
+- **Input:** RGB images (224x224)  
+- **Output:** 4 classes: `['Type_1', 'Type_2', 'Type_3', 'OOD']`  
+- **License:** MIT  
+- **Training dataset sources:**  
+  - Cervical images: Intel MobileODT competition dataset  
+  - OOD images: Caltech101 dataset  
+- **Preprocessing & Augmentation:**  
+  - Resize to 224x224  
+  - Normalization (ImageNet mean & std)  
+  - Data augmentation: Random rotation, color jitter (brightness/contrast)  
+
+### Dataset Distribution
+
+| Split      | Type_1 | Type_2 | Type_3 | OOD  | Total |
+| ---------- | ------ | ------ | ------ | ---- | ----- |
+| Train      | 557    | 532    | 547    | 424  | 2060  |
+| Validation | 151    | 161    | 154    | 122  | 588   |
+| Test       | 73     | 88     | 80     | 54   | 295   |
+
+### Training Details
+
+- Optimizer: Adam  
+- Loss: CrossEntropyLoss  
+- Batch size: 8  
+- Learning rate: 1e-5  
+- Epochs: 30  
+- Device: GPU (Tesla T4, 14GB)
+
+## Evaluation
+
+### Evaluation Metrics
+
+| Class   | Precision | Recall | F1-score | Sensitivity | Specificity |
+|---------|----------|--------|----------|-------------|-------------|
+| OOD     | 1.00     | 1.00   | 1.00     | 1.0000      | 1.0000      |
+| Type_1  | 0.74     | 0.93   | 0.82     | 0.9333      | 0.9074      |
+| Type_2  | 0.85     | 0.51   | 0.64     | 0.5114      | 0.9574      |
+| Type_3  | 0.73     | 0.92   | 0.81     | 0.9189      | 0.8762      |
+
+**Overall accuracy:** 0.81  
+
+**Confusion Matrix**
+```
+       Predicted
+        OOD  T1  T2  T3
+Actual
+OOD      54   0   0   0
+Type_1    0  56   3   1
+Type_2    0  19  45  24
+Type_3    0   1   5  68
+
+```
+
+**Classification Report**
+
+```
+              precision    recall  f1-score   support
+OOD           1.00      1.00      1.00        54
+Type_1        0.74      0.93      0.82        60
+Type_2        0.85      0.51      0.64        88
+Type_3        0.73      0.92      0.81        74
+
+accuracy                           0.81       276
+macro avg       0.83      0.84      0.82       276
+weighted avg    0.82      0.81      0.80       276
+
+```
+
+---
+
+## How to Get Started
+
+``````python
+import torch
+from torchvision import transforms, models
+from PIL import Image
+
+# Load model
+model = models.densenet121(pretrained=False)
+model.classifier = torch.nn.Linear(model.classifier.in_features, 4)
+model.load_state_dict(torch.load("Dense_net_121.pth", map_location="cpu"))
+model.eval()
+
+# Transform
+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])
+])
+
+# Load image
+image = Image.open("example.jpg").convert("RGB")
+image = transform(image).unsqueeze(0)
+
+# Predict
+outputs = model(image)
+probabilities = torch.softmax(outputs, dim=1)
+predicted_class = torch.argmax(probabilities, dim=1).item()
+confidence = probabilities[0, predicted_class].item()
+
+class_names = ["Type_1", "Type_2", "Type_3", "OOD"]
+print(f"Predicted class: {class_names[predicted_class]}, confidence: {confidence:.2f}")
+
+````
+
+---
+## Technical Specifications
+
+### Model Architecture
+
+* **Backbone:** DenseNet121 pretrained on ImageNet
+* **Output Layer:** Fully connected layer with 4 outputs (`Type_1`, `Type_2`, `Type_3`, `OOD`)
+* **Activation:** Softmax for multi-class classification
+* **Training Framework:** PyTorch
+* **Loss Function:** CrossEntropyLoss
+* **Data Handling:** Includes OOD images from Caltech101 along with in-distribution cervical images
+* **Preprocessing & Augmentation:** Resize to 224x224, normalization (ImageNet mean/std), random rotation, color jitter
+
+### Compute Infrastructure
+
+* **Hardware:** Tesla T4 GPU (14GB)
+* **Software:** PyTorch, torchvision, CUDA
+
+---