---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Paper_compared-beit-base
  results: []
---

<!-- 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. -->

# Paper_compared-beit-base

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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5363
- Accuracy: 0.8409

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6803        | 0.9492 | 14   | 0.9171          | 0.7156   |
| 0.8219        | 1.9661 | 29   | 0.5230          | 0.8330   |
| 0.2323        | 2.9831 | 44   | 0.5110          | 0.8047   |
| 0.1112        | 4.0    | 59   | 0.4968          | 0.8138   |
| 0.0387        | 4.9492 | 73   | 0.5502          | 0.8093   |
| 0.0232        | 5.9661 | 88   | 0.5506          | 0.8296   |
| 0.0096        | 6.9831 | 103  | 0.5341          | 0.8431   |
| 0.0068        | 8.0    | 118  | 0.6003          | 0.8149   |
| 0.0046        | 8.9492 | 132  | 0.5298          | 0.8409   |
| 0.0051        | 9.4915 | 140  | 0.5363          | 0.8409   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1