Machine Learning Training Utilities for TensorFlow 2.* and PyTorch with Python 3

To use MLTU in your own project, you can install it from PyPI:

pip install mltu

When running tutorials, it's necessary to install mltu for a specific tutorial, for example:

pip install mltu==0.1.3

Each tutorial has its own requirements.txt file for a specific mltu version. As this project progress, the newest versions may have breaking changes, so it's recommended to use the same version as in the tutorial.

1. Text Recognition With TensorFlow and CTC network, code in Tutorials\01_image_to_word folder;
2. TensorFlow OCR model for reading Captchas, code in Tutorials\02_captcha_to_text folder;
3. Handwriting words recognition with TensorFlow, code in Tutorials\03_handwriting_recognition folder;
4. Handwritten sentence recognition with TensorFlow, code in Tutorials\04_sentence_recognition folder;
5. Introduction to speech recognition with TensorFlow, code in Tutorials\05_speech_recognition folder;
6. Introduction to PyTorch in a practical way, code in Tutorials\06_pytorch_introduction folder;
7. Using custom wrapper to simplify PyTorch models training pipeline, code in Tutorials\07_pytorch_wrapper folder;
8. Handwriting words recognition with PyTorch, code in Tutorials\08_handwriting_recognition_torch folder;
9. Transformer training with TensorFlow for Translation task, code in Tutorials\09_translation_transformer folder;
10. Speech Recognition in Python | finetune wav2vec2 model for a custom ASR model, code in Tutorials\10_wav2vec2_torch folder;
11. YOLOv8: Real-Time Object Detection Simplified, code in Tutorials\11_Yolov8 folder;
12. YOLOv8: Customizing Object Detector training, code in Tutorials\11_Yolov8\train_yolov8.py folder;