
<!-- 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-patch16-224-in21k-finetuned-eurosat

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1770
- Accuracy: 0.9361

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.687         | 0.04  | 10   | 0.6778          | 0.6026   |
| 0.6605        | 0.09  | 20   | 0.6359          | 0.7564   |
| 0.6074        | 0.13  | 30   | 0.5734          | 0.7786   |
| 0.5464        | 0.17  | 40   | 0.4877          | 0.8267   |
| 0.4606        | 0.21  | 50   | 0.3836          | 0.8914   |
| 0.379         | 0.26  | 60   | 0.3269          | 0.8877   |
| 0.2746        | 0.3   | 70   | 0.2403          | 0.9198   |
| 0.2974        | 0.34  | 80   | 0.2931          | 0.8890   |
| 0.2459        | 0.39  | 90   | 0.2596          | 0.9016   |
| 0.2507        | 0.43  | 100  | 0.2366          | 0.9123   |
| 0.2627        | 0.47  | 110  | 0.2084          | 0.9224   |
| 0.2481        | 0.51  | 120  | 0.2050          | 0.9270   |
| 0.2372        | 0.56  | 130  | 0.2077          | 0.9267   |
| 0.2468        | 0.6   | 140  | 0.2111          | 0.9230   |
| 0.2272        | 0.64  | 150  | 0.1964          | 0.9267   |
| 0.2568        | 0.68  | 160  | 0.1975          | 0.9270   |
| 0.2608        | 0.73  | 170  | 0.2485          | 0.9048   |
| 0.2641        | 0.77  | 180  | 0.2143          | 0.9227   |
| 0.2347        | 0.81  | 190  | 0.1921          | 0.9307   |
| 0.2231        | 0.86  | 200  | 0.1882          | 0.9315   |
| 0.2147        | 0.9   | 210  | 0.1865          | 0.9329   |
| 0.2028        | 0.94  | 220  | 0.1901          | 0.9294   |
| 0.1792        | 0.98  | 230  | 0.1868          | 0.9297   |
| 0.2471        | 1.03  | 240  | 0.2104          | 0.9190   |
| 0.1896        | 1.07  | 250  | 0.1840          | 0.9321   |
| 0.2181        | 1.11  | 260  | 0.1800          | 0.9318   |
| 0.1861        | 1.16  | 270  | 0.1815          | 0.9305   |
| 0.1761        | 1.2   | 280  | 0.1886          | 0.9299   |
| 0.1703        | 1.24  | 290  | 0.1802          | 0.9315   |
| 0.184         | 1.28  | 300  | 0.1845          | 0.9321   |
| 0.1864        | 1.33  | 310  | 0.1791          | 0.9342   |
| 0.1857        | 1.37  | 320  | 0.1760          | 0.9347   |
| 0.1558        | 1.41  | 330  | 0.1798          | 0.9318   |
| 0.1852        | 1.45  | 340  | 0.1810          | 0.9323   |
| 0.183         | 1.5   | 350  | 0.1775          | 0.9321   |
| 0.2055        | 1.54  | 360  | 0.1789          | 0.9337   |
| 0.207         | 1.58  | 370  | 0.2082          | 0.9208   |
| 0.2264        | 1.63  | 380  | 0.1733          | 0.9339   |
| 0.1954        | 1.67  | 390  | 0.1772          | 0.9337   |
| 0.1676        | 1.71  | 400  | 0.1840          | 0.9302   |
| 0.1727        | 1.75  | 410  | 0.1784          | 0.9305   |
| 0.204         | 1.8   | 420  | 0.1731          | 0.9353   |
| 0.1805        | 1.84  | 430  | 0.1805          | 0.9310   |
| 0.1732        | 1.88  | 440  | 0.1773          | 0.9337   |
| 0.1831        | 1.93  | 450  | 0.1768          | 0.9337   |
| 0.1906        | 1.97  | 460  | 0.1967          | 0.9259   |
| 0.1785        | 2.01  | 470  | 0.1765          | 0.9331   |
| 0.1566        | 2.05  | 480  | 0.1749          | 0.9361   |
| 0.1612        | 2.1   | 490  | 0.1718          | 0.9342   |
| 0.1504        | 2.14  | 500  | 0.1770          | 0.9361   |
| 0.1704        | 2.18  | 510  | 0.1721          | 0.9363   |
| 0.1597        | 2.22  | 520  | 0.1711          | 0.9345   |
| 0.1283        | 2.27  | 530  | 0.1775          | 0.9361   |
| 0.1697        | 2.31  | 540  | 0.1722          | 0.9361   |
| 0.1541        | 2.35  | 550  | 0.1729          | 0.9366   |
| 0.1466        | 2.4   | 560  | 0.1708          | 0.9369   |
| 0.1604        | 2.44  | 570  | 0.1720          | 0.9371   |
| 0.1798        | 2.48  | 580  | 0.1718          | 0.9382   |
| 0.134         | 2.52  | 590  | 0.1733          | 0.9371   |
| 0.1215        | 2.57  | 600  | 0.1749          | 0.9369   |
| 0.1284        | 2.61  | 610  | 0.1760          | 0.9358   |
| 0.1449        | 2.65  | 620  | 0.1745          | 0.9361   |
| 0.214         | 2.7   | 630  | 0.1729          | 0.9382   |
| 0.1684        | 2.74  | 640  | 0.1724          | 0.9369   |
| 0.143         | 2.78  | 650  | 0.1737          | 0.9377   |
| 0.1491        | 2.82  | 660  | 0.1753          | 0.9366   |
| 0.1636        | 2.87  | 670  | 0.1743          | 0.9371   |
| 0.1672        | 2.91  | 680  | 0.1724          | 0.9377   |
| 0.1501        | 2.95  | 690  | 0.1720          | 0.9374   |


### Framework versions

- Transformers 4.35.0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.14.1

not_people people