---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_beit_base_f3
  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: 0.8837209302325582
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# hushem_40x_beit_base_f3

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9422
- Accuracy: 0.8837

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0594        | 1.0   | 108  | 0.4441          | 0.8372   |
| 0.0595        | 2.0   | 217  | 0.8725          | 0.8605   |
| 0.0173        | 3.0   | 325  | 0.5866          | 0.9070   |
| 0.0084        | 4.0   | 434  | 0.6360          | 0.8605   |
| 0.0005        | 5.0   | 542  | 0.6191          | 0.8837   |
| 0.0008        | 6.0   | 651  | 0.6635          | 0.9070   |
| 0.0008        | 7.0   | 759  | 0.8772          | 0.8837   |
| 0.0001        | 8.0   | 868  | 0.8012          | 0.9070   |
| 0.0           | 9.0   | 976  | 0.9139          | 0.8837   |
| 0.0001        | 9.95  | 1080 | 0.9422          | 0.8837   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1