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---

library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-oxford-iiit-pets
  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. -->

# vit-base-oxford-iiit-pets

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1798
- Accuracy: 0.9310

## 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: 0.0003

- train_batch_size: 512

- eval_batch_size: 8

- seed: 42

- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

- lr_scheduler_type: linear

- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 12   | 2.6101          | 0.5223   |
| No log        | 2.0   | 24   | 1.7190          | 0.8227   |
| No log        | 3.0   | 36   | 1.0833          | 0.8890   |
| No log        | 4.0   | 48   | 0.7011          | 0.9120   |
| No log        | 5.0   | 60   | 0.5052          | 0.9242   |
| No log        | 6.0   | 72   | 0.4097          | 0.9310   |
| No log        | 7.0   | 84   | 0.3560          | 0.9350   |
| No log        | 8.0   | 96   | 0.3237          | 0.9337   |
| 1.1364        | 9.0   | 108  | 0.3008          | 0.9378   |
| 1.1364        | 10.0  | 120  | 0.2833          | 0.9364   |
| 1.1364        | 11.0  | 132  | 0.2694          | 0.9391   |
| 1.1364        | 12.0  | 144  | 0.2586          | 0.9391   |
| 1.1364        | 13.0  | 156  | 0.2498          | 0.9418   |
| 1.1364        | 14.0  | 168  | 0.2423          | 0.9405   |
| 1.1364        | 15.0  | 180  | 0.2359          | 0.9405   |
| 1.1364        | 16.0  | 192  | 0.2303          | 0.9459   |
| 0.2326        | 17.0  | 204  | 0.2259          | 0.9405   |
| 0.2326        | 18.0  | 216  | 0.2222          | 0.9405   |
| 0.2326        | 19.0  | 228  | 0.2178          | 0.9432   |
| 0.2326        | 20.0  | 240  | 0.2146          | 0.9445   |
| 0.2326        | 21.0  | 252  | 0.2114          | 0.9432   |
| 0.2326        | 22.0  | 264  | 0.2087          | 0.9445   |
| 0.2326        | 23.0  | 276  | 0.2061          | 0.9432   |
| 0.2326        | 24.0  | 288  | 0.2040          | 0.9459   |
| 0.1651        | 25.0  | 300  | 0.2018          | 0.9459   |
| 0.1651        | 26.0  | 312  | 0.2000          | 0.9445   |
| 0.1651        | 27.0  | 324  | 0.1985          | 0.9459   |
| 0.1651        | 28.0  | 336  | 0.1968          | 0.9472   |
| 0.1651        | 29.0  | 348  | 0.1948          | 0.9459   |
| 0.1651        | 30.0  | 360  | 0.1939          | 0.9459   |
| 0.1651        | 31.0  | 372  | 0.1924          | 0.9459   |
| 0.1651        | 32.0  | 384  | 0.1915          | 0.9459   |
| 0.1651        | 33.0  | 396  | 0.1909          | 0.9459   |
| 0.134         | 34.0  | 408  | 0.1894          | 0.9472   |
| 0.134         | 35.0  | 420  | 0.1883          | 0.9459   |
| 0.134         | 36.0  | 432  | 0.1877          | 0.9472   |
| 0.134         | 37.0  | 444  | 0.1866          | 0.9486   |
| 0.134         | 38.0  | 456  | 0.1863          | 0.9472   |
| 0.134         | 39.0  | 468  | 0.1851          | 0.9486   |
| 0.134         | 40.0  | 480  | 0.1843          | 0.9472   |
| 0.134         | 41.0  | 492  | 0.1837          | 0.9472   |
| 0.1128        | 42.0  | 504  | 0.1831          | 0.9459   |
| 0.1128        | 43.0  | 516  | 0.1828          | 0.9472   |
| 0.1128        | 44.0  | 528  | 0.1822          | 0.9472   |
| 0.1128        | 45.0  | 540  | 0.1816          | 0.9472   |
| 0.1128        | 46.0  | 552  | 0.1808          | 0.9459   |
| 0.1128        | 47.0  | 564  | 0.1804          | 0.9459   |
| 0.1128        | 48.0  | 576  | 0.1802          | 0.9459   |
| 0.1128        | 49.0  | 588  | 0.1796          | 0.9459   |
| 0.0999        | 50.0  | 600  | 0.1793          | 0.9472   |
| 0.0999        | 51.0  | 612  | 0.1792          | 0.9486   |
| 0.0999        | 52.0  | 624  | 0.1787          | 0.9472   |
| 0.0999        | 53.0  | 636  | 0.1784          | 0.9472   |
| 0.0999        | 54.0  | 648  | 0.1780          | 0.9459   |
| 0.0999        | 55.0  | 660  | 0.1778          | 0.9445   |
| 0.0999        | 56.0  | 672  | 0.1772          | 0.9445   |
| 0.0999        | 57.0  | 684  | 0.1769          | 0.9472   |
| 0.0999        | 58.0  | 696  | 0.1768          | 0.9472   |
| 0.0894        | 59.0  | 708  | 0.1766          | 0.9472   |
| 0.0894        | 60.0  | 720  | 0.1763          | 0.9472   |
| 0.0894        | 61.0  | 732  | 0.1762          | 0.9486   |
| 0.0894        | 62.0  | 744  | 0.1760          | 0.9472   |
| 0.0894        | 63.0  | 756  | 0.1755          | 0.9459   |
| 0.0894        | 64.0  | 768  | 0.1752          | 0.9459   |
| 0.0894        | 65.0  | 780  | 0.1749          | 0.9459   |
| 0.0894        | 66.0  | 792  | 0.1749          | 0.9459   |
| 0.0828        | 67.0  | 804  | 0.1746          | 0.9472   |
| 0.0828        | 68.0  | 816  | 0.1745          | 0.9459   |
| 0.0828        | 69.0  | 828  | 0.1745          | 0.9459   |
| 0.0828        | 70.0  | 840  | 0.1744          | 0.9459   |
| 0.0828        | 71.0  | 852  | 0.1740          | 0.9459   |
| 0.0828        | 72.0  | 864  | 0.1741          | 0.9459   |
| 0.0828        | 73.0  | 876  | 0.1737          | 0.9459   |
| 0.0828        | 74.0  | 888  | 0.1739          | 0.9459   |
| 0.0778        | 75.0  | 900  | 0.1739          | 0.9459   |
| 0.0778        | 76.0  | 912  | 0.1737          | 0.9459   |
| 0.0778        | 77.0  | 924  | 0.1735          | 0.9459   |
| 0.0778        | 78.0  | 936  | 0.1733          | 0.9459   |
| 0.0778        | 79.0  | 948  | 0.1732          | 0.9459   |
| 0.0778        | 80.0  | 960  | 0.1732          | 0.9459   |
| 0.0778        | 81.0  | 972  | 0.1730          | 0.9459   |
| 0.0778        | 82.0  | 984  | 0.1730          | 0.9459   |
| 0.0778        | 83.0  | 996  | 0.1730          | 0.9459   |
| 0.0738        | 84.0  | 1008 | 0.1729          | 0.9459   |
| 0.0738        | 85.0  | 1020 | 0.1727          | 0.9459   |
| 0.0738        | 86.0  | 1032 | 0.1726          | 0.9459   |
| 0.0738        | 87.0  | 1044 | 0.1726          | 0.9459   |
| 0.0738        | 88.0  | 1056 | 0.1726          | 0.9459   |
| 0.0738        | 89.0  | 1068 | 0.1726          | 0.9459   |
| 0.0738        | 90.0  | 1080 | 0.1725          | 0.9459   |
| 0.0738        | 91.0  | 1092 | 0.1724          | 0.9459   |
| 0.0715        | 92.0  | 1104 | 0.1724          | 0.9459   |
| 0.0715        | 93.0  | 1116 | 0.1723          | 0.9459   |
| 0.0715        | 94.0  | 1128 | 0.1723          | 0.9459   |
| 0.0715        | 95.0  | 1140 | 0.1723          | 0.9459   |
| 0.0715        | 96.0  | 1152 | 0.1722          | 0.9459   |
| 0.0715        | 97.0  | 1164 | 0.1722          | 0.9459   |
| 0.0715        | 98.0  | 1176 | 0.1722          | 0.9459   |
| 0.0715        | 99.0  | 1188 | 0.1722          | 0.9459   |
| 0.0701        | 100.0 | 1200 | 0.1722          | 0.9459   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1
- Datasets 3.0.0
- Tokenizers 0.19.1