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fathyshalab
/
invoicevsadvertisement

Image Classification
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invoicevsadvertisement
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---
tags:
- generated_from_trainer
datasets:
- rvl_cdip
metrics:
- accuracy
model-index:
- name: invoicevsadvertisement
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: rvl_cdip
      type: rvl_cdip
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9892257579553997
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# invoicevsadvertisement

This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on the rvl_cdip dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0292
- Accuracy: 0.9892

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 192
- eval_batch_size: 192
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 768
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4353        | 0.98  | 41   | 0.0758          | 0.9837   |
| 0.0542        | 1.98  | 82   | 0.0359          | 0.9860   |
| 0.0349        | 2.98  | 123  | 0.0336          | 0.9867   |
| 0.0323        | 3.98  | 164  | 0.0304          | 0.9876   |
| 0.0288        | 4.98  | 205  | 0.0292          | 0.9892   |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.3.2
- Tokenizers 0.12.1