Building this project involved a multi-stage pipeline, from data curation to model deployment:
Data Collection & Preprocessing: I utilized the dataset. To ensure high-quality input, I applied techniques like data augmentation and normalization to handle noise. Model Architecture: I chose a Convolutional Neural Network (CNN) combined with Long Short-Term Memory (LSTM) units to capture both spatial features and temporal sequences. Tech Stack: Language: Python Frameworks: PyTorch / TensorFlow Libraries: NumPy, Pandas, Scikit-learn, and OpenCV for image processing.
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