---
base_model: microsoft/dit-base-finetuned-rvlcdip
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: dit-base-finetuned-rvlcdip
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.87
    - name: Precision
      type: precision
      value: 0.7623411371237458
    - name: Recall
      type: recall
      value: 0.87
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# dit-base-finetuned-rvlcdip

This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5940
- Accuracy: 0.87
- Precision: 0.7623
- Recall: 0.87
- F1 Score: 0.8126

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log        | 1.0   | 4    | 0.6006          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| No log        | 2.0   | 8    | 0.5169          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| No log        | 3.0   | 12   | 0.4027          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| 0.5427        | 4.0   | 16   | 0.3865          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| 0.5427        | 5.0   | 20   | 0.3894          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| 0.5427        | 6.0   | 24   | 0.3729          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| 0.5427        | 7.0   | 28   | 0.3707          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| 0.4458        | 8.0   | 32   | 0.3790          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| 0.4458        | 9.0   | 36   | 0.3504          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| 0.4458        | 10.0  | 40   | 0.3356          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| 0.4458        | 11.0  | 44   | 0.4082          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| 0.4369        | 12.0  | 48   | 0.3455          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| 0.4369        | 13.0  | 52   | 0.3074          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| 0.4369        | 14.0  | 56   | 0.3097          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| 0.4109        | 15.0  | 60   | 0.3173          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3