---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-fold3
  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: 0.8354430379746836
---

<!-- 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. -->

# beit-base-patch16-224-fold3

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5597
- Accuracy: 0.8354

## 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: 100

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.8571  | 3    | 0.8417          | 0.4810   |
| No log        | 2.0     | 7    | 0.6764          | 0.5823   |
| 0.71          | 2.8571  | 10   | 0.7272          | 0.5316   |
| 0.71          | 4.0     | 14   | 0.6122          | 0.6835   |
| 0.71          | 4.8571  | 17   | 0.6288          | 0.5949   |
| 0.6227        | 6.0     | 21   | 0.6550          | 0.5949   |
| 0.6227        | 6.8571  | 24   | 0.6240          | 0.6329   |
| 0.6227        | 8.0     | 28   | 0.5877          | 0.6709   |
| 0.5472        | 8.8571  | 31   | 0.7285          | 0.5823   |
| 0.5472        | 10.0    | 35   | 0.8305          | 0.5823   |
| 0.5472        | 10.8571 | 38   | 0.5102          | 0.7848   |
| 0.4766        | 12.0    | 42   | 0.5352          | 0.7215   |
| 0.4766        | 12.8571 | 45   | 0.5357          | 0.6962   |
| 0.4766        | 14.0    | 49   | 0.7418          | 0.6329   |
| 0.408         | 14.8571 | 52   | 0.6150          | 0.6835   |
| 0.408         | 16.0    | 56   | 0.4870          | 0.7975   |
| 0.408         | 16.8571 | 59   | 0.6427          | 0.6962   |
| 0.4078        | 18.0    | 63   | 0.4822          | 0.8101   |
| 0.4078        | 18.8571 | 66   | 0.4947          | 0.7975   |
| 0.3478        | 20.0    | 70   | 0.6847          | 0.7089   |
| 0.3478        | 20.8571 | 73   | 0.6154          | 0.7342   |
| 0.3478        | 22.0    | 77   | 0.5384          | 0.8101   |
| 0.3006        | 22.8571 | 80   | 0.5939          | 0.7595   |
| 0.3006        | 24.0    | 84   | 0.5214          | 0.7595   |
| 0.3006        | 24.8571 | 87   | 0.5452          | 0.7342   |
| 0.2977        | 26.0    | 91   | 0.6153          | 0.7215   |
| 0.2977        | 26.8571 | 94   | 0.4730          | 0.7975   |
| 0.2977        | 28.0    | 98   | 0.4861          | 0.7342   |
| 0.2768        | 28.8571 | 101  | 0.6705          | 0.7342   |
| 0.2768        | 30.0    | 105  | 0.6362          | 0.7848   |
| 0.2768        | 30.8571 | 108  | 0.6548          | 0.7848   |
| 0.2348        | 32.0    | 112  | 0.5100          | 0.7848   |
| 0.2348        | 32.8571 | 115  | 0.7156          | 0.7595   |
| 0.2348        | 34.0    | 119  | 0.4859          | 0.8228   |
| 0.2199        | 34.8571 | 122  | 0.8490          | 0.7342   |
| 0.2199        | 36.0    | 126  | 0.6095          | 0.7468   |
| 0.2199        | 36.8571 | 129  | 0.6427          | 0.7468   |
| 0.201         | 38.0    | 133  | 0.6283          | 0.7848   |
| 0.201         | 38.8571 | 136  | 0.8883          | 0.7595   |
| 0.1868        | 40.0    | 140  | 0.7146          | 0.7975   |
| 0.1868        | 40.8571 | 143  | 1.3800          | 0.6962   |
| 0.1868        | 42.0    | 147  | 0.5908          | 0.7848   |
| 0.2011        | 42.8571 | 150  | 0.6158          | 0.7722   |
| 0.2011        | 44.0    | 154  | 0.5477          | 0.7975   |
| 0.2011        | 44.8571 | 157  | 0.8354          | 0.7722   |
| 0.1807        | 46.0    | 161  | 0.7830          | 0.7848   |
| 0.1807        | 46.8571 | 164  | 0.6327          | 0.8228   |
| 0.1807        | 48.0    | 168  | 0.7858          | 0.7595   |
| 0.1579        | 48.8571 | 171  | 0.8322          | 0.7342   |
| 0.1579        | 50.0    | 175  | 0.7501          | 0.7848   |
| 0.1579        | 50.8571 | 178  | 0.8303          | 0.8228   |
| 0.2066        | 52.0    | 182  | 0.6831          | 0.7595   |
| 0.2066        | 52.8571 | 185  | 0.7837          | 0.8228   |
| 0.2066        | 54.0    | 189  | 0.5597          | 0.8354   |
| 0.1647        | 54.8571 | 192  | 0.5484          | 0.8354   |
| 0.1647        | 56.0    | 196  | 1.0047          | 0.7848   |
| 0.1647        | 56.8571 | 199  | 0.7815          | 0.8228   |
| 0.1404        | 58.0    | 203  | 0.6808          | 0.7975   |
| 0.1404        | 58.8571 | 206  | 1.0068          | 0.8101   |
| 0.1451        | 60.0    | 210  | 0.7698          | 0.8228   |
| 0.1451        | 60.8571 | 213  | 0.6495          | 0.8228   |
| 0.1451        | 62.0    | 217  | 0.7066          | 0.8354   |
| 0.1341        | 62.8571 | 220  | 0.6250          | 0.8354   |
| 0.1341        | 64.0    | 224  | 0.5573          | 0.7975   |
| 0.1341        | 64.8571 | 227  | 0.6051          | 0.8101   |
| 0.127         | 66.0    | 231  | 0.7576          | 0.8101   |
| 0.127         | 66.8571 | 234  | 0.8297          | 0.8101   |
| 0.127         | 68.0    | 238  | 1.0732          | 0.7975   |
| 0.1129        | 68.8571 | 241  | 1.0503          | 0.7975   |
| 0.1129        | 70.0    | 245  | 0.7520          | 0.8101   |
| 0.1129        | 70.8571 | 248  | 0.6825          | 0.8354   |
| 0.1205        | 72.0    | 252  | 0.7002          | 0.7975   |
| 0.1205        | 72.8571 | 255  | 0.7430          | 0.8101   |
| 0.1205        | 74.0    | 259  | 0.7610          | 0.7975   |
| 0.1199        | 74.8571 | 262  | 0.6854          | 0.8101   |
| 0.1199        | 76.0    | 266  | 0.6767          | 0.8354   |
| 0.1199        | 76.8571 | 269  | 0.6685          | 0.8354   |
| 0.1165        | 78.0    | 273  | 0.7134          | 0.7848   |
| 0.1165        | 78.8571 | 276  | 0.7344          | 0.7848   |
| 0.1213        | 80.0    | 280  | 0.7403          | 0.7722   |
| 0.1213        | 80.8571 | 283  | 0.7818          | 0.7848   |
| 0.1213        | 82.0    | 287  | 0.7620          | 0.7722   |
| 0.1024        | 82.8571 | 290  | 0.7539          | 0.7722   |
| 0.1024        | 84.0    | 294  | 0.7659          | 0.7722   |
| 0.1024        | 84.8571 | 297  | 0.7686          | 0.7848   |
| 0.1109        | 85.7143 | 300  | 0.7686          | 0.7848   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1