---
base_model: microsoft/dit-base-finetuned-rvlcdip
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: dit-base-finetuned-rvlcdip-finetuned-ind-17-imbalanced-aadhaarmask
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8458918688803746
    - name: Recall
      type: recall
      value: 0.8458918688803746
    - name: F1
      type: f1
      value: 0.8445087759723635
    - name: Precision
      type: precision
      value: 0.8462519380607423
---

<!-- 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. -->

# dit-base-finetuned-rvlcdip-finetuned-ind-17-imbalanced-aadhaarmask

This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3727
- Accuracy: 0.8459
- Recall: 0.8459
- F1: 0.8445
- Precision: 0.8463

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.9625        | 0.9974 | 293  | 0.8121          | 0.7812   | 0.7812 | 0.7600 | 0.7620    |
| 0.7711        | 1.9983 | 587  | 0.5780          | 0.8135   | 0.8135 | 0.7960 | 0.7843    |
| 0.555         | 2.9991 | 881  | 0.4868          | 0.8255   | 0.8255 | 0.8133 | 0.8133    |
| 0.6008        | 4.0    | 1175 | 0.4475          | 0.8357   | 0.8357 | 0.8281 | 0.8253    |
| 0.5318        | 4.9974 | 1468 | 0.4478          | 0.8267   | 0.8267 | 0.8221 | 0.8254    |
| 0.3382        | 5.9983 | 1762 | 0.3946          | 0.8463   | 0.8463 | 0.8412 | 0.8427    |
| 0.4307        | 6.9991 | 2056 | 0.4083          | 0.8344   | 0.8344 | 0.8317 | 0.8362    |
| 0.4613        | 8.0    | 2350 | 0.3915          | 0.8442   | 0.8442 | 0.8429 | 0.8481    |
| 0.3247        | 8.9974 | 2643 | 0.3758          | 0.8421   | 0.8421 | 0.8402 | 0.8395    |
| 0.3965        | 9.9745 | 2930 | 0.3637          | 0.8484   | 0.8484 | 0.8466 | 0.8470    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0a0+81ea7a4
- Datasets 2.19.0
- Tokenizers 0.19.1