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---

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_fold1
  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: 0.6413250067879446
---


<!-- 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-large-patch16_fold1

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: 1.4588
- Accuracy: 0.6413

## 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: 5



### Training results



| Training Loss | Epoch | Step | Validation Loss | Accuracy |

|:-------------:|:-----:|:----:|:---------------:|:--------:|

| 1.372         | 1.0   | 924  | 1.3390          | 0.5422   |

| 1.0566        | 2.0   | 1848 | 1.1838          | 0.5808   |

| 0.8724        | 3.0   | 2772 | 1.1155          | 0.6248   |

| 0.3326        | 4.0   | 3696 | 1.2344          | 0.6397   |

| 0.254         | 5.0   | 4620 | 1.4588          | 0.6413   |





### Framework versions



- Transformers 4.35.0

- Pytorch 2.1.0

- Datasets 2.14.6

- Tokenizers 0.14.1