---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_mingliangqiangu_model
  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. -->

# my_awesome_mingliangqiangu_model

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1140
- Accuracy: 0.9981

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7575        | 0.99  | 67   | 1.3989          | 0.9287   |
| 0.4806        | 2.0   | 135  | 0.4502          | 0.9935   |
| 0.2902        | 2.99  | 202  | 0.2922          | 0.9944   |
| 0.2073        | 4.0   | 270  | 0.2118          | 0.9981   |
| 0.1975        | 4.99  | 337  | 0.1831          | 0.9963   |
| 0.1514        | 6.0   | 405  | 0.1576          | 0.9935   |
| 0.1282        | 6.99  | 472  | 0.1290          | 1.0      |
| 0.1224        | 8.0   | 540  | 0.1317          | 0.9963   |
| 0.1147        | 8.99  | 607  | 0.1127          | 1.0      |
| 0.1129        | 9.93  | 670  | 0.1140          | 0.9981   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3