---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: deit-base-patch16-224-finetuned-ind-4-imbalanced-aadhaarmask-3839
  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: 0.9401041666666666
    - name: Recall
      type: recall
      value: 0.9401041666666666
    - name: F1
      type: f1
      value: 0.9384896500283729
    - name: Precision
      type: precision
      value: 0.9382242510101494
---

<!-- 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. -->

# deit-base-patch16-224-finetuned-ind-4-imbalanced-aadhaarmask-3839

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2241
- Accuracy: 0.9401
- Recall: 0.9401
- F1: 0.9385
- Precision: 0.9382

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.1426        | 1.0   | 96   | 0.2195          | 0.9297   | 0.9297 | 0.9263 | 0.9270    |
| 0.0644        | 2.0   | 192  | 0.2403          | 0.9245   | 0.9245 | 0.9249 | 0.9260    |
| 0.0695        | 3.0   | 288  | 0.3488          | 0.9232   | 0.9232 | 0.9221 | 0.9257    |
| 0.0674        | 4.0   | 384  | 0.2355          | 0.9375   | 0.9375 | 0.9366 | 0.9363    |
| 0.1265        | 5.0   | 480  | 0.2119          | 0.9388   | 0.9388 | 0.9376 | 0.9382    |
| 0.1128        | 6.0   | 576  | 0.2018          | 0.9401   | 0.9401 | 0.9388 | 0.9389    |
| 0.0806        | 7.0   | 672  | 0.2095          | 0.9388   | 0.9388 | 0.9371 | 0.9410    |
| 0.1237        | 8.0   | 768  | 0.2008          | 0.9427   | 0.9427 | 0.9423 | 0.9425    |
| 0.0955        | 9.0   | 864  | 0.1763          | 0.9440   | 0.9440 | 0.9420 | 0.9421    |
| 0.0429        | 10.0  | 960  | 0.2021          | 0.9401   | 0.9401 | 0.9381 | 0.9376    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1