| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - recall |
| - precision |
| model-index: |
| - name: dit-base-Document_Classification-RVL_CDIP |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: imagefolder |
| type: imagefolder |
| config: data |
| split: train |
| args: data |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.976678084687705 |
| language: |
| - en |
| --- |
| |
| # dit-base-Document_Classification-RVL_CDIP |
|
|
| This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base). |
|
|
| It achieves the following results on the evaluation set: |
| - Loss: 0.0786 |
| - Accuracy: 0.9767 |
| - Weighted f1: 0.9768 |
| - Micro f1: 0.9767 |
| - Macro f1: 0.9154 |
| - Weighted recall: 0.9767 |
| - Micro recall: 0.9767 |
| - Macro recall: 0.9019 |
| - Weighted precision: 0.9771 |
| - Micro precision: 0.9767 |
| - Macro precision: 0.9314 |
|
|
| ## Model description |
|
|
| For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Document%20AI/Multiclass%20Classification/Document%20Classification%20-%20RVL-CDIP/Document%20Classification%20-%20RVL-CDIP.ipynb |
|
|
| ## Intended uses & limitations |
|
|
| This model is intended to demonstrate my ability to solve a complex problem using technology. |
|
|
| ## Training and evaluation data |
|
|
| Dataset Source: https://www.kaggle.com/datasets/achrafbribiche/document-classification |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 128 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 3 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
| | 0.1535 | 1.0 | 208 | 0.1126 | 0.9622 | 0.9597 | 0.9622 | 0.5711 | 0.9622 | 0.9622 | 0.5925 | 0.9577 | 0.9622 | 0.5531 | |
| | 0.1195 | 2.0 | 416 | 0.0843 | 0.9738 | 0.9736 | 0.9738 | 0.8502 | 0.9738 | 0.9738 | 0.8037 | 0.9741 | 0.9738 | 0.9287 | |
| | 0.0979 | 3.0 | 624 | 0.0786 | 0.9767 | 0.9768 | 0.9767 | 0.9154 | 0.9767 | 0.9767 | 0.9019 | 0.9771 | 0.9767 | 0.9314 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.28.1 |
| - Pytorch 2.0.0 |
| - Datasets 2.11.0 |
| - Tokenizers 0.13.3 |
