---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-new-parameter
  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: 0.8518518518518519
    - name: Recall
      type: recall
      value: 0.8518518518518519
    - name: F1
      type: f1
      value: 0.8508141812977819
    - name: Precision
      type: precision
      value: 0.8576385720576808
---

<!-- 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. -->

# vit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-new-parameter

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3278
- Accuracy: 0.8519
- Recall: 0.8519
- F1: 0.8508
- Precision: 0.8576

## 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
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
| No log        | 1.0   | 1175  | 0.5572          | 0.8076   | 0.8076 | 0.7937 | 0.8043    |
| No log        | 2.0   | 2350  | 0.4673          | 0.8284   | 0.8284 | 0.8271 | 0.8347    |
| No log        | 3.0   | 3525  | 0.4109          | 0.8344   | 0.8344 | 0.8301 | 0.8367    |
| No log        | 4.0   | 4700  | 0.3984          | 0.8382   | 0.8382 | 0.8339 | 0.8375    |
| No log        | 5.0   | 5875  | 0.3886          | 0.8412   | 0.8412 | 0.8398 | 0.8467    |
| No log        | 6.0   | 7050  | 0.3520          | 0.8493   | 0.8493 | 0.8481 | 0.8519    |
| No log        | 7.0   | 8225  | 0.4229          | 0.8416   | 0.8416 | 0.8399 | 0.8512    |
| No log        | 8.0   | 9400  | 0.3140          | 0.8612   | 0.8612 | 0.8600 | 0.8656    |
| No log        | 9.0   | 10575 | 0.3399          | 0.8421   | 0.8421 | 0.8403 | 0.8464    |
| 0.4263        | 10.0  | 11750 | 0.3399          | 0.8476   | 0.8476 | 0.8468 | 0.8536    |


### Framework versions

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