In this Drive folder you can find all EasyOCR models in ONNX format covering all currently available languages. Look at the text file in the folder to see which one corresponds to the language you are using.
ONNX is an agnostic and standardized format for storing models. Models in ONNX format can be imported and used in several Runtimes including PyTorch, TensorFlow and OpenVINO. The ONNX team also provides its own runtine, ONNX Runtime. This Runtime allows development in several languages, operating systems and acceleration hardware. By owning the EasyOCR models in ONNX it is possible to cover new scenarios where the use of Python and/or PyTorch is not adequate.
In my use case, I am using the EasyOCR and ONNX Runtime models to be able to develop C++ applications. This makes it much easier to bring EasyOCR functionalities into production.
In this issue you can find a general guide on how I have managed to modify EasyOCR to export the models, initially developed in Torch to ONNX. Note that the export process explained in the guide can only be achieved if you have CUDA acceleration. As not everyone has access to such hardware, I have decided to share a copy of the models already exported.
The models that I make available are a first version, I may optimize the exported models in the next weeks.
In this Drive folder you can find all EasyOCR models in ONNX format covering all currently available languages. Look at the text file in the folder to see which one corresponds to the language you are using.
ONNX is an agnostic and standardized format for storing models. Models in ONNX format can be imported and used in several Runtimes including PyTorch, TensorFlow and OpenVINO. The ONNX team also provides its own runtine, ONNX Runtime. This Runtime allows development in several languages, operating systems and acceleration hardware. By owning the EasyOCR models in ONNX it is possible to cover new scenarios where the use of Python and/or PyTorch is not adequate.
In my use case, I am using the EasyOCR and ONNX Runtime models to be able to develop C++ applications. This makes it much easier to bring EasyOCR functionalities into production.
In this issue you can find a general guide on how I have managed to modify EasyOCR to export the models, initially developed in Torch to ONNX. Note that the export process explained in the guide can only be achieved if you have CUDA acceleration. As not everyone has access to such hardware, I have decided to share a copy of the models already exported.
The models that I make available are a first version, I may optimize the exported models in the next weeks.