: Video data is memory-intensive. Use data generators to load MP4 batches on the fly rather than keeping the entire dataset in RAM.
When developing the training loop in Python, prioritize high-fidelity data handling:
: Avoid artifacts by ensuring consistent compression settings if you are pre-generating videos for scientific or sharp-line plot analysis. 4. Deployment and Integration
: Use a Vision Transformer (ViT) backend to process frame embeddings, applying temporal attention to understand the relationship between different points in the video sequence.
: If your model has a limited context window, remove redundant frames using similarity thresholds to focus on meaningful motion. Normalization : Resize frames to a standard dimension (e.g., ) and normalize pixel values to a 2. Select a Model Architecture
: Effective for capturing spatial and temporal features simultaneously.
Based on the course's focus on sequence models and attention, your "piece" or model should likely utilize:
) at the Technion, where likely refers to the fourth programming assignment or a specific project task involving video data or sequence models.