---
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-11745
  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: 0.8352490421455939
    - name: Recall
      type: recall
      value: 0.8352490421455939
    - name: F1
      type: f1
      value: 0.8308048120146843
    - name: Precision
      type: precision
      value: 0.8376123328771266
---

<!-- 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-11745

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: 0.3657
- Accuracy: 0.8352
- Recall: 0.8352
- F1: 0.8308
- Precision: 0.8376

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.5885        | 0.9974 | 293  | 0.5902          | 0.7910   | 0.7910 | 0.7773 | 0.7940    |
| 0.4253        | 1.9983 | 587  | 0.4608          | 0.8174   | 0.8174 | 0.8128 | 0.8149    |
| 0.4941        | 2.9991 | 881  | 0.4111          | 0.8335   | 0.8335 | 0.8309 | 0.8369    |
| 0.4876        | 4.0    | 1175 | 0.4002          | 0.8378   | 0.8378 | 0.8349 | 0.8426    |
| 0.3936        | 4.9974 | 1468 | 0.3882          | 0.8246   | 0.8246 | 0.8221 | 0.8361    |
| 0.3928        | 5.9983 | 1762 | 0.3640          | 0.8421   | 0.8421 | 0.8404 | 0.8443    |
| 0.3752        | 6.9991 | 2056 | 0.3553          | 0.8442   | 0.8442 | 0.8409 | 0.8469    |
| 0.2994        | 8.0    | 2350 | 0.3540          | 0.8259   | 0.8259 | 0.8245 | 0.8271    |
| 0.2614        | 8.9974 | 2643 | 0.3591          | 0.8378   | 0.8378 | 0.8360 | 0.8386    |
| 0.2632        | 9.9745 | 2930 | 0.3483          | 0.8404   | 0.8404 | 0.8399 | 0.8419    |


### Framework versions

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