sbt-idp/cope2n-ai-fi/modules/sdsvkie/README.md
2023-12-12 15:14:54 +07:00

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<p align="center">
<h1 align="center">SDSVKIE</h1>
</p>
***Feature***
- Extract information from documents: VAT Invoice, Receipt
- Language: VI + EN
***What's news***
### - Ver 1.0.1:
- Improve postprocessing for receipts
- Support handling multiple pages for PDF files
- Lastest weight: /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/best
- Lastest config: /mnt/ssd1T/hoanglv/Projects/KIE/sdsvkie/workdirs/sdsap_receipt/exp_9_lr5e_6_no_scheduler/config.yaml
## I. Setup
***Dependencies***
- Python: 3.8
- Torch: 1.10.2
- CUDA: 11.6
- transformers: 4.28.1
```
pip install -v -e .
```
## II. Inference
```
from sdsvkie import Predictor
import cv2
predictor = Predictor(
cfg="./workdirs/training/sdsap_receipt/exp_3/config.yaml",
weights="./workdirs/training/sdsap_receipt/exp_3/best",
device="cpu",
)
img = cv2.imread("./demos/4 Sep OPC to Home.jpg")
out = predictor(img)
output = out['end2end_results']
```
## III. Training
- Prepare dataset: The structure of the dataset directory is organized as follows:
└── base_dataset \
├── train \
├──── sub_dir_1 \
├────── img1.txt \
├────── img1.txt \
├────── ... \
├──── sub_dir_2 \
├────── img2.txt \
├────── img2.txt \
├── test \
├──── imgn.jpg \
├──── imgn.txt
- Edit and run scripts:
```
sh ./scripts/train.sh
```
# TODO
- [ ] Add more fields: sub_total, tips, seller_address, item list
- [x] Support muliple pages
- [x] Review result KIE for invoice (vnpt_exp_4_model)
- [x] Fix unnormalize box error in some cases
- [x] Support multiple pages
- [x] Create 200 multiple pages invoice
- [ ] Finalize multi page testset
- [ ] Eval result