vit_receipts_classifier
This model is a fine-tuned version of
google/vit-base-patch16-224-in21k on the cord, rvl-cdip, visual-genome and an external receipt dataset to carry out Binary Classification (
ticket vs
no_ticket).
Ticket here is used as a synonym to "receipt".
It achieves the following results on the evaluation set, which contain pictures from the above datasets in scanned, photography or mobile picture formats (color and grayscale):
Model description
This model is a Binary Classifier finetuned version of ViT, to predict if an input image is a picture / scan of receipts(s) o something else.
Intended uses & limitations
Use this model to classify your images into tickets or not tickers. WIth the tickets group, you can use Multimodal Information Extraction, as Visual Named Entity Recognition, to extract the ticket items, amounts, total, etc. Check the Cord dataset for more information.
Training and evaluation data
This model used 2 datasets as positive class (ticket):
cordhttps://expressexpense.com/blog/free-receipt-images-ocr-machine-learning-dataset/
For the negative class (no_ticket), the following datasets were used:
- A subset of
RVL-CDIP
- A subset of
visual-genome
Training procedure
Datasets were loaded with different distributions of data for positive and negative classes. Then, normalization and resizing is carried out to adapt it to ViT expected input.
Different runs were carried out changing the data distribution and the hyperparameters to maximize F1.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|
| 0.0026 | 0.28 | 500 | 0.0187 | 0.9982 |
| 0.0186 | 0.56 | 1000 | 0.0116 | 0.9991 |
| 0.0006 | 0.84 | 1500 | 0.0044 | 0.9997 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.11.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
