| # 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 | |
| ## 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 | |
| # Define the model architecture | |
| resnet50 = models.resnet50(pretrained=False) | |
| # Modify the last layer to match the number of classes (11) | |
| num_ftrs = resnet50.fc.in_features | |
| resnet50.fc = torch.nn.Linear(num_ftrs, 11) | |
| # Load trained model weights | |
| resnet50.load_state_dict(torch.load("fine_tuned_model/pytorch_model.bin")) | |
| resnet50.eval() # Set model to evaluation mode | |
| print("Model loaded successfully!") | |
| # Load class names | |
| with open("fine_tuned_model/classes.txt", "r") as f: | |
| class_names = f.read().splitlines() | |
| print("Classes:", class_names) | |
| # Define image transformations (same as training) | |
| transform = transforms.Compose([ | |
| transforms.Resize((224, 224)), # Resize to match ResNet-50 input size | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # Normalization | |
| ]) | |
| # Load the custom image | |
| image_path = "/kaggle/input/sample-image-1/pickup_truck_sample_image.jpg" # Change this to your test image path | |
| image = Image.open(image_path).convert("RGB") # Open image and convert to RGB | |
| input_tensor = transform(image).unsqueeze(0) # Add batch dimension | |
| # Move to GPU if available | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| resnet50 = resnet50.to(device) | |
| input_tensor = input_tensor.to(device) | |
| # Get predictions | |
| with torch.no_grad(): | |
| outputs = resnet50(input_tensor) | |
| _, predicted_class = torch.max(outputs, 1) # Get the class with highest score | |
| # Print the result | |
| 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. | |