---
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-17-imbalanced-aadhaarmask-14687
  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: 0.843745130911636
    - name: Precision
      type: precision
      value: 0.8521498018011563
---

<!-- 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-17-imbalanced-aadhaarmask-14687

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: 0.3635
- Accuracy: 0.8459
- Recall: 0.8459
- F1: 0.8437
- Precision: 0.8521

## 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: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6153        | 0.9974  | 293  | 0.6607          | 0.7739   | 0.7739 | 0.7506 | 0.7444    |
| 0.5075        | 1.9983  | 587  | 0.5850          | 0.7927   | 0.7927 | 0.7767 | 0.8081    |
| 0.5278        | 2.9991  | 881  | 0.4721          | 0.8199   | 0.8199 | 0.8170 | 0.8301    |
| 0.445         | 4.0     | 1175 | 0.4495          | 0.8186   | 0.8186 | 0.8136 | 0.8192    |
| 0.3781        | 4.9974  | 1468 | 0.4018          | 0.8263   | 0.8263 | 0.8249 | 0.8326    |
| 0.4025        | 5.9983  | 1762 | 0.4356          | 0.8221   | 0.8221 | 0.8195 | 0.8245    |
| 0.3409        | 6.9991  | 2056 | 0.3876          | 0.8267   | 0.8267 | 0.8248 | 0.8330    |
| 0.3181        | 8.0     | 2350 | 0.3849          | 0.8391   | 0.8391 | 0.8372 | 0.8436    |
| 0.3042        | 8.9974  | 2643 | 0.3850          | 0.8280   | 0.8280 | 0.8285 | 0.8347    |
| 0.2475        | 9.9983  | 2937 | 0.3624          | 0.8493   | 0.8493 | 0.8475 | 0.8571    |
| 0.2339        | 10.9991 | 3231 | 0.3865          | 0.8318   | 0.8318 | 0.8281 | 0.8307    |
| 0.2455        | 12.0    | 3525 | 0.3337          | 0.8387   | 0.8387 | 0.8371 | 0.8433    |
| 0.2127        | 12.9974 | 3818 | 0.3685          | 0.8306   | 0.8306 | 0.8281 | 0.8356    |
| 0.2288        | 13.9983 | 4112 | 0.3545          | 0.8370   | 0.8370 | 0.8352 | 0.8385    |
| 0.2534        | 14.9991 | 4406 | 0.3587          | 0.8429   | 0.8429 | 0.8398 | 0.8537    |
| 0.1911        | 16.0    | 4700 | 0.3573          | 0.8387   | 0.8387 | 0.8367 | 0.8396    |
| 0.2118        | 16.9974 | 4993 | 0.3676          | 0.8370   | 0.8370 | 0.8356 | 0.8415    |
| 0.22          | 17.9983 | 5287 | 0.3469          | 0.8357   | 0.8357 | 0.8326 | 0.8412    |
| 0.1938        | 18.9991 | 5581 | 0.3512          | 0.8365   | 0.8365 | 0.8343 | 0.8363    |
| 0.1816        | 19.9489 | 5860 | 0.3323          | 0.8463   | 0.8463 | 0.8449 | 0.8476    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0a0+81ea7a4
- Datasets 2.18.0
- Tokenizers 0.19.1