mirror of
https://github.com/qurator-spk/eynollah.git
synced 2026-05-31 01:59:27 +02:00
13f2f81c45
- comment out ad-hoc conversion/loading of autosized models - refactor predictor backends for model types into separate functions - only attempt inference conversion of cnn-rnn-ocr model if applicable (`ctc_loss` layer still present) - apply VRAM limits across model types (Keras, TF-Serving, ONNX) - apply TF device selection across model types (Keras, TF-Serving) - implement predictor backend for ONNX models: - using onnxruntime - covering CUDA and TensorRT providers - trying to support manual device selection - hiding session management details - converting float32 to float16 |
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| .. | ||
| cli | ||
| model_zoo | ||
| training | ||
| utils | ||
| __init__.py | ||
| Charis-Regular.ttf | ||
| extract_images.py | ||
| eynollah.py | ||
| eynollah_ocr.py | ||
| image_enhancer.py | ||
| mb_ro_on_layout.py | ||
| ocrd-tool.json | ||
| ocrd_cli.py | ||
| ocrd_cli_binarization.py | ||
| patch_encoder.py | ||
| plot.py | ||
| predictor.py | ||
| processor.py | ||
| sbb_binarize.py | ||
| writer.py | ||