---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-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: 0.8450404427415922
    - name: Recall
      type: recall
      value: 0.8450404427415922
    - name: F1
      type: f1
      value: 0.8442233792705293
    - name: Precision
      type: precision
      value: 0.8494143266059094
---

<!-- 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. -->

# beit-base-patch16-224-pt22k-ft22k-finetuned-ind-17-imbalanced-aadhaarmask

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3480
- Accuracy: 0.8450
- Recall: 0.8450
- F1: 0.8442
- Precision: 0.8494

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.5859        | 0.9974 | 293  | 0.6117          | 0.8114   | 0.8114 | 0.7891 | 0.8139    |
| 0.5281        | 1.9983 | 587  | 0.4362          | 0.8442   | 0.8442 | 0.8375 | 0.8484    |
| 0.4214        | 2.9991 | 881  | 0.4228          | 0.8438   | 0.8438 | 0.8392 | 0.8529    |
| 0.4221        | 4.0    | 1175 | 0.4121          | 0.8382   | 0.8382 | 0.8331 | 0.8495    |
| 0.4127        | 4.9974 | 1468 | 0.3692          | 0.8476   | 0.8476 | 0.8454 | 0.8511    |
| 0.3122        | 5.9983 | 1762 | 0.3741          | 0.8408   | 0.8408 | 0.8394 | 0.8462    |
| 0.3079        | 6.9991 | 2056 | 0.3628          | 0.8429   | 0.8429 | 0.8403 | 0.8445    |
| 0.2851        | 8.0    | 2350 | 0.3635          | 0.8412   | 0.8412 | 0.8389 | 0.8412    |
| 0.297         | 8.9974 | 2643 | 0.3407          | 0.8510   | 0.8510 | 0.8497 | 0.8545    |
| 0.2109        | 9.9745 | 2930 | 0.3566          | 0.8421   | 0.8421 | 0.8406 | 0.8418    |


### Framework versions

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