mirror of
https://github.com/qurator-spk/eynollah.git
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27f43c175f
major conflicts resolved manually:
- branches for non-`light` segmentation already removed in main
- Keras/TF setup and no TF1 sessions, esp. in new ModelZoo
- changes to binarizer and its CLI (`mode`, `overwrite`, `run_single()`)
- writer: `build...` w/ kwargs instead of positional
- training for segmentation/binarization/enhancement tasks:
* drop unused `generate_data_from_folder()`
* simplify `preprocess_imgs()`: turn `preprocess_img()`, `get_patches()`
and `get_patches_num_scale_new()` into generators, only writing
result files in the caller (top-level loop) instead of passing
output directories and file counter
- training for new OCR task:
* `train`: put keys into additional `config_params` where they belong,
resp. (conditioned under existing keys), and w/ better documentation
* `train`: add new keys as kwargs to `run()` to make usable
* `utils`: instead of custom data loader `data_gen_ocr()`, re-use
existing `preprocess_imgs()` (for cfg capture and top-level loop),
but extended w/ new kwargs and calling new `preprocess_img_ocr()`;
the latter as single-image generator (also much simplified)
* `train`: use tf.data loader pipeline from that generator w/ standard
mechanisms for batching, shuffling, prefetching etc.
* `utils` and `train`: instead of `vectorize_label`, use `Dataset.padded_batch`
* add TensorBoard callback and re-use our checkpoint callback
* also use standard Keras top-level loop for training
still problematic (substantially unresolved):
- `Patches` now only w/ fixed implicit size
(ignoring training config params)
- `PatchEncoder` now only w/ fixed implicit num patches and projection dim
(ignoring training config params)
10 lines
214 B
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10 lines
214 B
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# ocrd includes opencv, numpy, shapely, click
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ocrd >= 3.3.0
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numpy < 2.0
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scikit-learn >= 0.23.2
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tensorflow
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tf-keras # avoid keras 3 (also needs TF_USE_LEGACY_KERAS=1)
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numba <= 0.58.1
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scikit-image
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biopython
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tabulate
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