Model Archive File

The MLTK uses an archive file ( to store the relevant model information.


The model archive file is automatically created after running the train command and is updated after running the evaluate, quantize, and update_params commands.

The model archive file uses the standard Zip File Format
and its name has the format: <model name> where <model name> is the name of the MLTK model.

The model archive file is useful as it allows for grouping the various training and evaluation files into a single, distributable file.
This file can also be directly loaded by many MLTK commands and Python APIs, e.g.:

mltk profile ~/


The model archive file stores a given model’s:

  • Model specification Python script

  • Trained model files (.tflite, .h5)

  • Training logs

  • Evaluation logs

Directory Structure

Assume we have the following model archive file ~/workspace/ The contents of this archive would have the following contents:

/                   - The model specification script
/my_model.tflite               - The quantized model which can programmed onto an embedded device
/my_model.h5                   - The trained, non-quantized, Keras model
/my_model.h5.summary.txt       - A text summary of the .h5 model
/my_model.tflite.summary.txt   - A text summary of the .tflite model
/train/log.txt                 - Log file generated during training
/train/training-history.png    - Training history diagram
/train/training-history.json   - Training history in JSON format
/eval/h5/                      - Evaluation results from the .h5 (i.e. non-quantized) model
/eval/tflite/                  - Evaluation results from the .tflite (i.e. quantized) model