---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Boya1_RMSProp_1-e5_20Epoch_09Momentum_Beit-large-patch16_fold2
  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: 0.6483783783783784
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Boya1_RMSProp_1-e5_20Epoch_09Momentum_Beit-large-patch16_fold2

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: 1.6891
- Accuracy: 0.6484

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2166        | 1.0   | 923  | 1.1497          | 0.6165   |
| 0.9097        | 2.0   | 1846 | 1.0420          | 0.6449   |
| 0.7046        | 3.0   | 2769 | 1.0073          | 0.6589   |
| 0.4498        | 4.0   | 3692 | 1.0708          | 0.6619   |
| 0.3558        | 5.0   | 4615 | 1.1984          | 0.6481   |
| 0.2479        | 6.0   | 5538 | 1.3200          | 0.6535   |
| 0.136         | 7.0   | 6461 | 1.4614          | 0.6508   |
| 0.1353        | 8.0   | 7384 | 1.5768          | 0.6551   |
| 0.0896        | 9.0   | 8307 | 1.6599          | 0.65     |
| 0.1222        | 10.0  | 9230 | 1.6891          | 0.6484   |


### Framework versions

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