---
library_name: transformers
license: apache-2.0
base_model: ricardoSLabs/pre_CIDAUTv2
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: pre_CIDAUTv3
  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: 0.9841269841269841
---

<!-- 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. -->

# pre_CIDAUTv3

This model is a fine-tuned version of [ricardoSLabs/pre_CIDAUTv2](https://huggingface.co./ricardoSLabs/pre_CIDAUTv2) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1209
- Accuracy: 0.9841

## 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: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.8889 | 4    | 0.5177          | 0.7937   |
| No log        | 2.0    | 9    | 0.3085          | 0.8889   |
| 1.0669        | 2.8889 | 13   | 0.2461          | 0.8889   |
| 1.0669        | 4.0    | 18   | 0.1346          | 0.9683   |
| 0.2558        | 4.4444 | 20   | 0.1209          | 0.9841   |


### Framework versions

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