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Releases: ankandrew/fast-plate-ocr

fast-plate-ocr 1.0.0

30 Jun 02:53
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What's Changed

Added

  • New XS and S CCT model variants, trained with x3 more data (220k global plates).
  • Complete rework of docs, documenting much more everything.
  • Inference now works smoothly with different onnxruntime variants like onnxruntime-gpu, onnxruntime-openvino, etc.
  • Support for building and customizing CCT (Compact Convolutional Transformer) models from YAML configs.
  • New model building logic, allows users to build custom-based architectures while validating it with Pydantic.
  • New metric val_plate_len_acc.
  • Added support for categorical focal loss.
  • Added more test coverage (configs, train scripts, etc.).
  • New validate_dataset.py script to help check datasets before training.
  • Export script now officially supports more formats like TFLite and CoreML.
  • New plate config support: keep_aspect_ratio, interpolation, image_color_mode and padding_color.
  • New default augmentation for RGB image mode.
  • New default models, trained with much more data.
  • Added examples and more docs.

Changed

  • Visualize augmentation script now respects config-based preprocessing.
  • Improved plate config validation.
  • ONNXPlateRecognizer is now called LicensePlateRecognizer.

Full Changelog: v0.3.0...v1.0.0

fast-plate-ocr 0.3.0

08 Dec 16:42
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What's Changed

Full Changelog: v0.2.0...v0.3.0

fast-plate-ocr 0.2.0

14 Oct 04:04
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What's Changed

  • New European model using MobileViTV2 - trained on +40 countries 🚀 .
  • New models and Improved logging by @ankandrew in #19 ⭐.

New Contributors

Full Changelog: v0.1.6...v0.2.0

fast-plate-ocr 0.1.6

10 May 22:17
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Full Changelog: v0.1.5...v0.1.6

Added

  • Add new Argentinian model trained with more (synthetic) data.
  • Add option to visualize only predictions which have low char prob.
  • Add onnxsim for simplifying ONNX model when exporting.

fast-plate-ocr 0.1.5

01 May 19:48
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fast-plate-ocr 0.1.3

01 May 19:36
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fast-plate-ocr 0.1.2

12 Apr 21:02
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HUB

08 Apr 20:33
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HUB

Trained models HUB and related assets (.keras and .onnx).

Additional datasets

@biodatasciencearg for contributing arg_plate_dataset_biodatasciencearg.zip dataset.