DeepSolo-ONNX

This is a slimmed down version of the code for the scene text recognition model DeepSolo. See the original codebase here: https://github.com/ViTAE-Transformer/DeepSolo

DeepSolo-ONNX is a simple way to export a checkpoint of the DeepSolo model to ONNX for inference on any of the supported platforms.

Installing

There is no need to install DeepSolo-ONNX or any of its dependencies locally, see the next section for what you should do instead.

Using

You can use this Google Colab notebook to convert a checkpoint of your choosing to ONNX in 4 easy steps.

Original checkpoint

You will need to supply the original model's checkpoint for conversion. You can get these at the main DeepSolo repository: https://github.com/ViTAE-Transformer/DeepSolo

I save my checkpoints in Google Drive for easy and repeatable access from Colab. But simply uploading to the instance's /content folder also works fine.

Custom character maps

This is only relevant for models trained for different character sets.

If you are trying to convert a model you've trained yourself for some language other than English, which is supported by default by the base model, you'll need to adjust the vocabulary settings.

Create an alphabet

You'll need to create a file named "lang-chars-#.txt" in directory './char-dicts' where "lang" is the name of the language and "#" is the total number of characters in the language's alphabet. The file should contain a literal string, comprised of the langauge's characters.

You can refer to the files 'heb-chars-29.txt' and 'heb-chars-71.txt' for examples of such files.

Once the text file with the alphabet is ready, run the make_dicts.py script in the projects root:

python make_dicts.py

This will rebuild all alphabet files and create corresponding '.cmap' files, which are simply pickled lists of characters.

Adjust VOC_SIZE

You will also need to adjust the MODEL.TRANSFORMER.VOC_SIZE setting.

The best way to do that is to create a new config file, by copying the file './configs/Base_det_export.yaml' under a descriptive name and adding a line in the MODEL.TRANSFORMER block (between the lines 11 and 33):

VOC_SIZE = 37

setting it to whatever number of characters is in your language's alphabet.

Specify the new config

Replace the base config in the Google Colab notebook with your version (uploading the new config or saving it to Drive).

Done.

Running inference

Look at notebooks/inference.ipynb for an example of how to perform inference with a single image.

Output format

The model's run() method returns a list with a tensor containing:

- center line prediction logits;
- center line point coordinates;
- text prediction logits;
- bounding box points coordinate.