Product Requirements Document (PRD)¶
Product Vision¶
Create a robust and efficient quantization evaluation pipeline for SegFormer models, enabling to assess and compare the performance of various quantization methods in semantic segmentation tasks.
Target Users¶
- Machine Learning Researchers
- Computer Vision Engineers
- MLOps Professionals
Key Features¶
1. Model Loading and Quantization¶
- Support for loading pre-trained SegFormer models from a specified path or URL.
- Implementation of multiple quantization methods (float8, int8, int4, int2) to reduce model size and potentially improve inference speed.
2. Dataset Processing¶
- Efficient handling of large datasets through sharding to manage memory usage and processing time.
- Support for Scene Parse 150 dataset for semantic segmentation tasks.
3. Evaluation Pipeline¶
- Comprehensive evaluation metrics (mean IoU, mean accuracy, overall accuracy) to assess model performance.
- Batch processing for efficient evaluation of models on large datasets.
4. Experiment Tracking¶
- Integration with Weights & Biases for logging and visualizing results, including model size, performance metrics, and experiment metadata.
- Automatic logging of model size and performance metrics to track changes over time.
5. Modular Design¶
- Easy extension to support additional models and datasets through a modular architecture.
- Flexible configuration options to customize the evaluation process.
Success Criteria¶
- Successfully evaluate SegFormer models with different quantization levels.
- Achieve a balance between model size reduction and performance maintenance to ensure practical deployment.
- Provide clear, actionable insights through experiment tracking and visualization to guide model optimization.
Future Enhancements¶
- Support for additional semantic segmentation datasets to broaden the scope of evaluation.
- Integration of more quantization methods to explore different quantization strategies.
- Automated hyperparameter tuning for optimal quantization settings.