---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: ditmodel
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: train
      split: train
      args: train
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9512195121951219
---

<!-- 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. -->

# ditmodel

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1182
- Accuracy: 0.9512
- Weighted f1: 0.9515
- Micro f1: 0.9512
- Macro f1: 0.9473
- Weighted recall: 0.9512
- Micro recall: 0.9512
- Macro recall: 0.9498
- Weighted precision: 0.9527
- Micro precision: 0.9512
- Macro precision: 0.9458

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.1916        | 0.98  | 38   | 0.1396          | 0.9461   | 0.9465      | 0.9461   | 0.9408   | 0.9461          | 0.9461       | 0.9427       | 0.9487             | 0.9461          | 0.9412          |
| 0.1597        | 1.99  | 77   | 0.1227          | 0.9520   | 0.9523      | 0.9520   | 0.9485   | 0.9520          | 0.9520       | 0.9515       | 0.9541             | 0.9520          | 0.9472          |
| 0.1722        | 2.94  | 114  | 0.1182          | 0.9512   | 0.9515      | 0.9512   | 0.9473   | 0.9512          | 0.9512       | 0.9498       | 0.9527             | 0.9512          | 0.9458          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.6.1
- Tokenizers 0.15.1