🚗 Vehicle Classification using Deep Learning
🚗 Vehicle Classification using Deep Learning | ResNet18 | Full Project Demo
In this video, I present my deep learning project on Vehicle Classification, where I built an end-to-end system to classify vehicles into different categories such as ambulance, bike, bus, car, and truck.
This project uses PyTorch and a pretrained ResNet18 model to achieve accurate and efficient predictions using transfer learning.
🔍 What’s Covered in This Video
✔️ Dataset explanation (train/test structure)
✔️ Data preprocessing & augmentation techniques
✔️ Model architecture using ResNet18
✔️ Training process & hyperparameters
✔️ Evaluation metrics (Accuracy, Confusion Matrix, Classification Report)
✔️ Live prediction using a web application
⚙️ Technologies Used
Python 🐍
PyTorch
Flask
NumPy, Pandas
Matplotlib
🎯 Key Features
✨ Transfer Learning for faster training
✨ Data Augmentation to improve generalization
✨ Model checkpointing based on validation accuracy
✨ Real-time prediction through web interface
📊 Model Details
Model: ResNet18 (Pretrained on ImageNet)
Loss Function: CrossEntropyLoss
Optimizer: Adam
Learning Rate: 1e-4
🚀 Live Demo
Upload an image and get instant vehicle classification results using the trained model!
📌 Tags
#DeepLearning #MachineLearning #PyTorch #ComputerVision #ResNet18 #AIProject #VehicleClassification #FlaskApp #AI #DataScience
🙌 Thank You for Watching!
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