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User Story

Title: Efficient Model Quantization and Evaluation for SegFormer

As a Machine Learning Engineer working on semantic segmentation tasks,

I want to have a streamlined pipeline for loading, quantizing, and evaluating SegFormer models,

So that I can quickly assess the performance impact of different quantization methods on model accuracy and efficiency.

Acceptance Criteria

  1. Model Loading:
  • The system should load pre-trained SegFormer models from a specified path or URL.
  • It should support loading models with different quantization levels (float8, int8, int4, int2).
  • The loading process should be logged, including time taken and memory footprint.
  1. Quantization:
  • Implement functions to apply quantization techniques to the loaded models.
  • Ensure that quantization does not significantly degrade model performance.
  • Provide options for different quantization methods and allow for easy switching between them.
  1. Dataset Handling:
  • Load and preprocess the Scene Parse 150 dataset or any other specified dataset for semantic segmentation.
  • Implement data sharding to manage large datasets efficiently, allowing for batch processing.
  • Convert images and annotations into the required format for model input.
  1. Evaluation:
  • Run the quantized models on the dataset to generate predictions.
  • Calculate key metrics like mean IoU, mean accuracy, and overall accuracy.
  • Log these metrics to Weights & Biases for tracking and visualization.
  1. Performance Metrics:
  • Track and report the inference speed for each quantization level.
  • Compare the memory footprint of the original model versus quantized versions.
  1. Configuration:
  • Allow for configuration of model paths, dataset paths, quantization methods, and evaluation parameters through a configuration file (config.py).
  1. Error Handling and Logging:
  • Implement comprehensive error handling to manage issues like model loading failures, dataset processing errors, or quantization issues.
  • Use Python’s logging module to log all operations, errors, and performance metrics.
  1. User Interface:
  • Provide a command-line interface for running the evaluation pipeline, allowing users to specify which quantization methods to test, dataset to use, etc.
  1. Documentation:
  • Include detailed documentation on how to set up the environment, run the pipeline, and interpret the results.

  1. Testing:

    • Ensure unit tests are in place for each module to verify functionality.
    • Integration tests should cover the entire pipeline from model loading to evaluation.

Out of Scope

  • Training new models or fine-tuning existing ones.
  • Real-time processing or deployment of models in production environments.

Additional Notes

  • The system should be designed with scalability in mind, allowing for future expansion to include other models or datasets.
  • Consideration for security, especially in handling API keys for Weights & Biases, should be integrated into the design.