---
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: 0.9479166666666666
    - name: Recall
      type: recall
      value: 0.9479166666666666
    - name: F1
      type: f1
      value: 0.9464668525772705
    - name: Precision
      type: precision
      value: 0.9472181024490807
---

<!-- 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: 0.1444
- Accuracy: 0.9479
- Recall: 0.9479
- F1: 0.9465
- Precision: 0.9472

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.2268        | 1.0   | 96   | 0.2805          | 0.8919   | 0.8919 | 0.8753 | 0.8798    |
| 0.2356        | 2.0   | 192  | 0.2842          | 0.9023   | 0.9023 | 0.8950 | 0.8967    |
| 0.1597        | 3.0   | 288  | 0.2120          | 0.9219   | 0.9219 | 0.9116 | 0.9211    |
| 0.1349        | 4.0   | 384  | 0.2449          | 0.9206   | 0.9206 | 0.9146 | 0.9193    |
| 0.1647        | 5.0   | 480  | 0.2226          | 0.9167   | 0.9167 | 0.9129 | 0.9121    |
| 0.1117        | 6.0   | 576  | 0.1599          | 0.9453   | 0.9453 | 0.9415 | 0.9434    |
| 0.1232        | 7.0   | 672  | 0.1698          | 0.9492   | 0.9492 | 0.9477 | 0.9485    |
| 0.1317        | 8.0   | 768  | 0.1624          | 0.9388   | 0.9388 | 0.9365 | 0.9368    |
| 0.1018        | 9.0   | 864  | 0.1648          | 0.9388   | 0.9388 | 0.9360 | 0.9355    |
| 0.0828        | 10.0  | 960  | 0.1597          | 0.9453   | 0.9453 | 0.9435 | 0.9454    |


### Framework versions

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