---
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-base-patch16_fold4
  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: 0.6552845528455284
---

<!-- 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. -->

# Boya1_RMSProp_1-e5_20Epoch_09Momentum_Beit-base-patch16_fold4

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: 3.1034
- Accuracy: 0.6553

## 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: 0.0001
- 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: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1966        | 1.0   | 923   | 1.1276          | 0.5995   |
| 1.0326        | 2.0   | 1846  | 1.0374          | 0.6360   |
| 0.6666        | 3.0   | 2769  | 1.0904          | 0.6415   |
| 0.4288        | 4.0   | 3692  | 1.2437          | 0.6474   |
| 0.2209        | 5.0   | 4615  | 1.4346          | 0.6404   |
| 0.1143        | 6.0   | 5538  | 1.6952          | 0.6442   |
| 0.1733        | 7.0   | 6461  | 1.9268          | 0.6547   |
| 0.0409        | 8.0   | 7384  | 2.2016          | 0.6518   |
| 0.0999        | 9.0   | 8307  | 2.4623          | 0.6485   |
| 0.0104        | 10.0  | 9230  | 2.6094          | 0.6534   |
| 0.0424        | 11.0  | 10153 | 2.7340          | 0.6558   |
| 0.0463        | 12.0  | 11076 | 2.8098          | 0.6599   |
| 0.0005        | 13.0  | 11999 | 2.9333          | 0.6553   |
| 0.0144        | 14.0  | 12922 | 2.9705          | 0.6531   |
| 0.0002        | 15.0  | 13845 | 3.0020          | 0.6566   |
| 0.0157        | 16.0  | 14768 | 3.0642          | 0.6588   |
| 0.0005        | 17.0  | 15691 | 3.0529          | 0.6575   |
| 0.0029        | 18.0  | 16614 | 3.0952          | 0.6558   |
| 0.0024        | 19.0  | 17537 | 3.0982          | 0.6572   |
| 0.0001        | 20.0  | 18460 | 3.1034          | 0.6553   |


### Framework versions

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