---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_1x_beit_base_sgd_001_fold5
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.84
---

<!-- 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. -->

# smids_1x_beit_base_sgd_001_fold5

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: 0.3960
- Accuracy: 0.84

## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0915        | 1.0   | 75   | 1.0482          | 0.46     |
| 0.8641        | 2.0   | 150  | 0.8453          | 0.6317   |
| 0.8005        | 3.0   | 225  | 0.7497          | 0.67     |
| 0.7495        | 4.0   | 300  | 0.6945          | 0.7067   |
| 0.7055        | 5.0   | 375  | 0.6553          | 0.7467   |
| 0.7202        | 6.0   | 450  | 0.6245          | 0.75     |
| 0.6588        | 7.0   | 525  | 0.6019          | 0.7583   |
| 0.6049        | 8.0   | 600  | 0.5871          | 0.76     |
| 0.6317        | 9.0   | 675  | 0.5605          | 0.7833   |
| 0.5775        | 10.0  | 750  | 0.5437          | 0.785    |
| 0.5951        | 11.0  | 825  | 0.5303          | 0.7933   |
| 0.5297        | 12.0  | 900  | 0.5191          | 0.79     |
| 0.5261        | 13.0  | 975  | 0.5051          | 0.7933   |
| 0.5545        | 14.0  | 1050 | 0.4974          | 0.7983   |
| 0.4597        | 15.0  | 1125 | 0.4949          | 0.805    |
| 0.4273        | 16.0  | 1200 | 0.4837          | 0.8017   |
| 0.4781        | 17.0  | 1275 | 0.4758          | 0.8067   |
| 0.4613        | 18.0  | 1350 | 0.4662          | 0.815    |
| 0.4966        | 19.0  | 1425 | 0.4609          | 0.8133   |
| 0.5166        | 20.0  | 1500 | 0.4558          | 0.81     |
| 0.4529        | 21.0  | 1575 | 0.4548          | 0.8167   |
| 0.4333        | 22.0  | 1650 | 0.4478          | 0.8233   |
| 0.4673        | 23.0  | 1725 | 0.4422          | 0.8183   |
| 0.402         | 24.0  | 1800 | 0.4383          | 0.8283   |
| 0.4207        | 25.0  | 1875 | 0.4375          | 0.8283   |
| 0.4343        | 26.0  | 1950 | 0.4301          | 0.8267   |
| 0.4249        | 27.0  | 2025 | 0.4265          | 0.8283   |
| 0.4127        | 28.0  | 2100 | 0.4255          | 0.8267   |
| 0.4286        | 29.0  | 2175 | 0.4192          | 0.8367   |
| 0.3988        | 30.0  | 2250 | 0.4174          | 0.8367   |
| 0.3838        | 31.0  | 2325 | 0.4145          | 0.8383   |
| 0.3896        | 32.0  | 2400 | 0.4157          | 0.835    |
| 0.4348        | 33.0  | 2475 | 0.4153          | 0.825    |
| 0.41          | 34.0  | 2550 | 0.4109          | 0.8367   |
| 0.3989        | 35.0  | 2625 | 0.4069          | 0.84     |
| 0.3824        | 36.0  | 2700 | 0.4101          | 0.8367   |
| 0.3688        | 37.0  | 2775 | 0.4062          | 0.8367   |
| 0.4091        | 38.0  | 2850 | 0.4063          | 0.8367   |
| 0.3672        | 39.0  | 2925 | 0.4039          | 0.835    |
| 0.4219        | 40.0  | 3000 | 0.4009          | 0.8383   |
| 0.4047        | 41.0  | 3075 | 0.4024          | 0.8383   |
| 0.4168        | 42.0  | 3150 | 0.3989          | 0.835    |
| 0.4198        | 43.0  | 3225 | 0.3971          | 0.8417   |
| 0.4236        | 44.0  | 3300 | 0.3971          | 0.84     |
| 0.3959        | 45.0  | 3375 | 0.3975          | 0.8433   |
| 0.3933        | 46.0  | 3450 | 0.3984          | 0.8433   |
| 0.3443        | 47.0  | 3525 | 0.3963          | 0.8417   |
| 0.3626        | 48.0  | 3600 | 0.3958          | 0.8417   |
| 0.38          | 49.0  | 3675 | 0.3960          | 0.8417   |
| 0.3733        | 50.0  | 3750 | 0.3960          | 0.84     |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0