Comments (2)
Hi, if you meant about the IIT-CDIP dataset, it is taking lot of time for making the dataset and then saving it (since pytesseract is slow). So, authors would be releasing the dataset used in pre-trained method soon, so maybe we can wait till that time, for obtaining the performance of our implementation on the same dataset
If you are talking about the code, you can go through the following steps:
- Import create_features, and apply it on each image and save it in pickle file
- Create a dataset, which can read the pickle file and then return the features,target
- Create a dataloader object, and then loop through each entry and apply the model
- Backpropagate the loss
I hope this helps
from docformer.
Looking forward to releasing the pre-training dataset!
from docformer.
Related Issues (20)
- Reference code of pre-training tasks HOT 4
- [Errno 2] No such file or directory: 'rvl_cdip_dataset.csv'
- NER task HOT 4
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- Unable to convert model to onnx HOT 3
- DocFormer for Token Classification. HOT 3
- Shape mismatch during sanity check HOT 1
- finetune the Docformer HOT 8
- Pre-trained models HOT 5
- Error in Example: Please provide the bounding box and words or pass the argument "use_ocr" = True HOT 1
- DocFormer for key-value pairs extraction HOT 1
- Using pre-trained models HOT 17
- Inference for token classification. HOT 1
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- Permission Denied Pretraining Weights HOT 2
- NotImplementedError: Support for `validation_epoch_end` has been removed in v2.0.0. `DocFormer` implements this method HOT 2
- pre-training code tutorial HOT 3
- The pre-trained weight link is invalid HOT 1
- DocFormerv2 HOT 1
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from docformer.