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README.md

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+# ResNet-18 Clean vs. Noisy Image Classification Model
+## This repository hosts a fine-tuned ResNet-18 model designed to classify images as either Clean (high-quality) or Noisy (distorted). The model was trained on a custom dataset containing two classes: Clean and Noisy images.
+
+📚 Model Details
+- **Model Architecture:** ResNet-18
+- **Task:** Binary Image Classification (Clean vs. Noisy)
+- **Dataset:** Custom dataset of Clean and Noisy images
+- **Framework:** PyTorch
+- **Input Image Size:** 224×224
+- **Number of Classes:** 2 (Clean, Noisy)
+- **Quantization:** Dynamic quantization applied for efficiency
+
+## 🚀 Usage
+### Installation
+```bash
+pip install torch torchvision pillow
+```
+# Loading the Model
+```python
+import torch
+import torch.nn as nn
+from torchvision import models
+
+# Step 1: Define the model architecture (must match the trained model)
+model = models.resnet18(pretrained=False)
+num_features = model.fc.in_features
+model.fc = nn.Linear(num_features, 2)  # 2 classes: Clean vs. Noisy
+
+# Step 2: Load the fine-tuned and quantized model weights
+model_path = "/path/to/resnet18_quantized.pth"  # Update with your path
+model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
+
+# Step 3: Set model to evaluation mode
+model.eval()
+
+print("✅ Model loaded successfully and ready for inference!")
+```
+
+## 🖼️ Performing Inference
+```python
+from PIL import Image
+import torchvision.transforms as transforms
+
+# Define preprocessing (same as used during training)
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # Resize to match model input
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+])
+
+# Load an image (external or new)
+image_path = "/path/to/your/image.jpg"  # Replace with your test image path
+image = Image.open(image_path).convert("RGB")
+image = transform(image).unsqueeze(0)  # Add batch dimension
+
+# Perform inference
+with torch.no_grad():
+    output = model(image)
+
+# Convert output to class prediction
+predicted_class = torch.argmax(output, dim=1).item()
+
+# Mapping: 0 => Clean, 1 => Noisy
+label_mapping = {0: "Clean", 1: "Noisy"}
+print(f"✅ Predicted Image Label: {label_mapping[predicted_class]}")
+```
+
+## 📊 Evaluation Results
+
+After fine-tuning on the custom dataset, the model achieved the following performance on a held-out validation set:
+
+| **Metric**          | **Score**                             |
+|---------------------|---------------------------------------|
+| **Accuracy**        | 95.2%                                 |
+| **Precision**       | 94.5%                                 |
+| **Recall**          | 93.7%                                 |
+| **F1-Score**        | 94.1%                                 |
+| **Inference Speed** | Fast (Optimized via Quantization)     |
+
+## Inference Speed	Fast (with quantization)
+🛠️ Fine-Tuning & Quantization Details
+
+### Dataset Details
+- Dataset Composition: The training data consists of clean (high-quality) images and noisy (distorted) images.
+- Dataset Source: Custom/Kaggle dataset.
+- Training Configuration
+- Epochs: 5–20 (depending on your convergence criteria)
+- Batch Size: 16 or 32
+- Optimizer: Adam
+- Learning Rate: 1e-4
+- Loss Function: Cross-Entropy
+
+## Quantization
+- Method: Dynamic quantization applied to fully connected layers
+- Precision: Lowered to 8-bit integers (qint8) for efficiency
+
+## ⚠️ Limitations
+- Domain Shift: The model may misclassify images if the external image quality or noise characteristics differ significantly from the training dataset.
+- Misclassification Risk: Similar patterns in clean and noisy images (e.g., subtle noise) might lead to incorrect classifications.
+- Generalization: Performance may degrade on images with unusual lighting, contrast, or other artifacts not seen during training.