---
base_model: microsoft/dit-base
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: 0.8741148701809599
---

<!-- 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 is a fine-tuned version of [microsoft/dit-base](https://huggingface.co./microsoft/dit-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2285
- Accuracy: 0.8741
- Weighted f1: 0.8720
- Micro f1: 0.8741
- Macro f1: 0.8640
- Weighted recall: 0.8741
- Micro recall: 0.8741
- Macro recall: 0.8626
- Weighted precision: 0.8780
- Micro precision: 0.8741
- Macro precision: 0.8745

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.8637        | 0.98  | 38   | 0.5986          | 0.6778   | 0.5949      | 0.6778   | 0.5662   | 0.6778          | 0.6778       | 0.6474       | 0.7506             | 0.6778          | 0.7511          |
| 0.5018        | 1.99  | 77   | 0.2707          | 0.8517   | 0.8453      | 0.8517   | 0.8377   | 0.8517          | 0.8517       | 0.8345       | 0.8588             | 0.8517          | 0.8623          |
| 0.3761        | 2.94  | 114  | 0.2285          | 0.8741   | 0.8720      | 0.8741   | 0.8640   | 0.8741          | 0.8741       | 0.8626       | 0.8780             | 0.8741          | 0.8745          |


### Framework versions

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