---
library_name: transformers
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: CIDAUTv2
  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: 0.9259259259259259
---

<!-- 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. -->

# CIDAUTv2

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: 0.2719
- Accuracy: 0.9259

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 4    | 0.7322          | 0.5880   |
| No log        | 2.0   | 8    | 0.6585          | 0.5972   |
| 0.7438        | 3.0   | 12   | 0.6115          | 0.7222   |
| 0.7438        | 4.0   | 16   | 0.5726          | 0.7546   |
| 0.5781        | 5.0   | 20   | 0.4803          | 0.7824   |
| 0.5781        | 6.0   | 24   | 0.4627          | 0.8333   |
| 0.5781        | 7.0   | 28   | 0.4060          | 0.8056   |
| 0.4511        | 8.0   | 32   | 0.3512          | 0.8796   |
| 0.4511        | 9.0   | 36   | 0.2725          | 0.9028   |
| 0.296         | 10.0  | 40   | 0.2719          | 0.9259   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0