Create README.md

README.md

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+# ResNet-50 Fine-Tuned Model for Vehicle Type Classification
+
+This repository hosts a **fine-tuned ResNet-50 model** for **Vehicle Type Classification**, trained on a subset of the **MIO-TCD Traffic Dataset**. This model is designed for **traffic management applications**, enabling real-time and accurate recognition of different vehicle types, such as cars, trucks, buses, and motorcycles.
+
+## Model Details
+
+- **Model Architecture:** ResNet-50
+- **Task:** Vehicle Type Classification
+- **Dataset:** MIO-TCD (Subset from Kaggle: `miotcd-dataset-50000-imagesclassification`)
+- **Number of Classes:** 11 vehicle categories
+- **Fine-tuning Framework:** PyTorch (`torchvision.models.resnet50`)
+- **Optimization:** Trained with Adam optimizer and data augmentation for robust performance
+
+## Downloading the Model
+
+You can download the fine-tuned model from the provided link:
+
+```sh
+wget <download_link>/fine_tuned_model.zip
+unzip fine_tuned_model.zip
+```
+
+## Usage
+
+### Installation
+
+Ensure you have the required dependencies installed:
+
+```sh
+pip install torch torchvision pillow
+```
+
+### Loading the Model
+
+```python
+import torch
+import torchvision.models as models
+import torchvision.transforms as transforms
+from PIL import Image
+
+# Load the model architecture
+model = models.resnet50(pretrained=False)
+num_ftrs = model.fc.in_features
+model.fc = torch.nn.Linear(num_ftrs, 11)  # 11 vehicle classes
+
+# Load fine-tuned weights
+model.load_state_dict(torch.load("fine_tuned_model/pytorch_model.bin", map_location=torch.device('cpu')))
+model.eval()  # Set to evaluation mode
+
+# Load class labels
+with open("fine_tuned_model/classes.txt", "r") as f:
+    class_names = f.read().splitlines()
+
+# Define preprocessing transformations
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+# Load and preprocess a test image
+image_path = "path_to_your_image.jpg"  # Change this to your test image path
+image = Image.open(image_path).convert("RGB")
+input_tensor = transform(image).unsqueeze(0)
+
+# Make prediction
+with torch.no_grad():
+    outputs = model(input_tensor)
+    _, predicted_class = torch.max(outputs, 1)
+
+print(f"Predicted Vehicle Type: {class_names[predicted_class.item()]}")
+```
+
+## Performance Metrics
+
+- **Validation Accuracy:** High accuracy achieved on the test dataset
+- **Inference Speed:** Optimized for real-time classification
+- **Robustness:** Trained with data augmentation to handle variations in lighting and angles
+
+## Dataset Details
+
+The dataset consists of **50,000 images** across **11 vehicle types**, structured in the following folders:
+- **articulated_truck**
+- **bicycle**
+- **bus**
+- **car**
+- **motorcycle**
+- **non-motorized_vehicle**
+- **pedestrian**
+- **pickup_truck**
+- **single_unit_truck**
+- **work_van**
+- **unknown**
+
+### Training Details
+
+- **Number of Epochs:** 10
+- **Batch Size:** 32
+- **Optimizer:** Adam
+- **Learning Rate:** 1e-4
+- **Loss Function:** Cross-Entropy Loss
+- **Data Augmentation:** Horizontal flipping, random cropping, normalization
+
+## Repository Structure
+
+```
+.
+├── fine_tuned_model/      # Contains the fine-tuned model files
+│   ├── pytorch_model.bin  # Model weights
+│   ├── classes.txt        # Class labels
+├── dataset/               # Training dataset (MIO-TCD subset)
+├── scripts/               # Training and evaluation scripts
+├── README.md              # Model documentation
+```
+
+## Limitations
+
+- The model is trained specifically on the **MIO-TCD dataset** and may not generalize well to images from different sources.
+- Accuracy may vary based on real-world conditions such as lighting, occlusion, and camera angles.
+- Requires GPU for faster inference.
+
+## Contributing
+
+Contributions are welcome! If you have suggestions for improvement, feel free to submit a pull request or open an issue.
+
+