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

# results

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1114
- Accuracy: 0.9687

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6639        | 0.1829 | 100  | 0.6155          | 0.6554   |
| 0.4191        | 0.3657 | 200  | 0.3088          | 0.8959   |
| 0.1698        | 0.5486 | 300  | 0.5321          | 0.7281   |
| 0.0749        | 0.7314 | 400  | 0.5087          | 0.7900   |
| 0.0484        | 0.9143 | 500  | 0.4649          | 0.8185   |
| 0.0323        | 1.0971 | 600  | 0.6888          | 0.762    |
| 0.0264        | 1.28   | 700  | 0.1395          | 0.9513   |
| 0.0224        | 1.4629 | 800  | 0.0661          | 0.9776   |
| 0.02          | 1.6457 | 900  | 0.1173          | 0.9581   |
| 0.0168        | 1.8286 | 1000 | 0.3498          | 0.889    |
| 0.013         | 2.0114 | 1100 | 0.1053          | 0.9655   |
| 0.0087        | 2.1943 | 1200 | 0.3601          | 0.8947   |
| 0.0081        | 2.3771 | 1300 | 0.1508          | 0.9535   |
| 0.0073        | 2.56   | 1400 | 0.2090          | 0.9390   |
| 0.0056        | 2.7429 | 1500 | 0.1136          | 0.9649   |
| 0.005         | 2.9257 | 1600 | 0.2656          | 0.9206   |
| 0.0036        | 3.1086 | 1700 | 0.1320          | 0.9595   |
| 0.002         | 3.2914 | 1800 | 0.1068          | 0.9686   |
| 0.0018        | 3.4743 | 1900 | 0.1091          | 0.9690   |
| 0.0019        | 3.6571 | 2000 | 0.1114          | 0.9687   |
| 0.0018        | 3.84   | 2100 | 0.0968          | 0.9719   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1