---
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: test
      split: train
      args: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9523326572008114
---

<!-- 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 fintuned on DiT model for document classification on custom dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1482
- Accuracy: 0.9523
- Weighted f1: 0.9524
- Micro f1: 0.9523
- Macro f1: 0.9505
- Weighted recall: 0.9523
- Micro recall: 0.9523
- Macro recall: 0.9523
- Weighted precision: 0.9544
- Micro precision: 0.9523
- Macro precision: 0.9506

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.2337        | 1.0   | 78   | 0.2668          | 0.9087   | 0.9098      | 0.9087   | 0.9058   | 0.9087          | 0.9087       | 0.9040       | 0.9229             | 0.9087          | 0.9220          |
| 0.1711        | 2.0   | 156  | 0.1820          | 0.9376   | 0.9380      | 0.9376   | 0.9331   | 0.9376          | 0.9376       | 0.9403       | 0.9416             | 0.9376          | 0.9292          |
| 0.1297        | 3.0   | 234  | 0.1482          | 0.9523   | 0.9524      | 0.9523   | 0.9505   | 0.9523          | 0.9523       | 0.9523       | 0.9544             | 0.9523          | 0.9506          |


### Framework versions

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