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# vit-base-patch16-224-finetuned-eurosat

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8960
- Model Preparation Time: 0.0037

## 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: 100

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Model Preparation Time |
|:-------------:|:-------:|:----:|:---------------:|:----------------------:|
| 7.7174        | 0.9874  | 59   | 7.7848          | 0.0037                 |
| 7.6016        | 1.9916  | 119  | 7.7339          | 0.0037                 |
| 7.4761        | 2.9958  | 179  | 7.6441          | 0.0037                 |
| 7.2852        | 4.0     | 239  | 7.5057          | 0.0037                 |
| 7.083         | 4.9874  | 298  | 7.3286          | 0.0037                 |
| 6.8119        | 5.9916  | 358  | 7.1090          | 0.0037                 |
| 6.5497        | 6.9958  | 418  | 6.8711          | 0.0037                 |
| 6.1656        | 8.0     | 478  | 6.6169          | 0.0037                 |
| 5.8334        | 8.9874  | 537  | 6.3286          | 0.0037                 |
| 5.3878        | 9.9916  | 597  | 6.0292          | 0.0037                 |
| 5.0134        | 10.9958 | 657  | 5.7486          | 0.0037                 |
| 4.6087        | 12.0    | 717  | 5.4834          | 0.0037                 |
| 4.2544        | 12.9874 | 776  | 5.2186          | 0.0037                 |
| 3.8669        | 13.9916 | 836  | 4.9842          | 0.0037                 |
| 3.5993        | 14.9958 | 896  | 4.7566          | 0.0037                 |
| 3.2331        | 16.0    | 956  | 4.5623          | 0.0037                 |
| 2.9124        | 16.9874 | 1015 | 4.3663          | 0.0037                 |
| 2.6122        | 17.9916 | 1075 | 4.1944          | 0.0037                 |
| 2.466         | 18.9958 | 1135 | 4.0160          | 0.0037                 |
| 2.2074        | 20.0    | 1195 | 3.8582          | 0.0037                 |
| 2.0851        | 20.9874 | 1254 | 3.7160          | 0.0037                 |
| 1.8354        | 21.9916 | 1314 | 3.5740          | 0.0037                 |
| 1.7343        | 22.9958 | 1374 | 3.4548          | 0.0037                 |
| 1.5804        | 24.0    | 1434 | 3.3600          | 0.0037                 |
| 1.3193        | 24.9874 | 1493 | 3.2336          | 0.0037                 |
| 1.328         | 25.9916 | 1553 | 3.1294          | 0.0037                 |
| 1.163         | 26.9958 | 1613 | 3.0355          | 0.0037                 |
| 1.0761        | 28.0    | 1673 | 2.9737          | 0.0037                 |
| 0.9834        | 28.9874 | 1732 | 2.8952          | 0.0037                 |
| 0.9141        | 29.9916 | 1792 | 2.7900          | 0.0037                 |
| 0.8862        | 30.9958 | 1852 | 2.7381          | 0.0037                 |
| 0.7757        | 32.0    | 1912 | 2.6868          | 0.0037                 |
| 0.7475        | 32.9874 | 1971 | 2.6134          | 0.0037                 |
| 0.6518        | 33.9916 | 2031 | 2.5770          | 0.0037                 |
| 0.6766        | 34.9958 | 2091 | 2.5278          | 0.0037                 |
| 0.5741        | 36.0    | 2151 | 2.5009          | 0.0037                 |
| 0.5877        | 36.9874 | 2210 | 2.4436          | 0.0037                 |
| 0.4996        | 37.9916 | 2270 | 2.4148          | 0.0037                 |
| 0.5316        | 38.9958 | 2330 | 2.3809          | 0.0037                 |
| 0.4896        | 40.0    | 2390 | 2.3330          | 0.0037                 |
| 0.501         | 40.9874 | 2449 | 2.3055          | 0.0037                 |
| 0.4052        | 41.9916 | 2509 | 2.3000          | 0.0037                 |
| 0.398         | 42.9958 | 2569 | 2.2854          | 0.0037                 |
| 0.3702        | 44.0    | 2629 | 2.2536          | 0.0037                 |
| 0.3629        | 44.9874 | 2688 | 2.2342          | 0.0037                 |
| 0.3729        | 45.9916 | 2748 | 2.2190          | 0.0037                 |
| 0.3206        | 46.9958 | 2808 | 2.2078          | 0.0037                 |
| 0.38          | 48.0    | 2868 | 2.1726          | 0.0037                 |
| 0.3379        | 48.9874 | 2927 | 2.1600          | 0.0037                 |
| 0.3248        | 49.9916 | 2987 | 2.1453          | 0.0037                 |
| 0.3577        | 50.9958 | 3047 | 2.1153          | 0.0037                 |
| 0.2946        | 52.0    | 3107 | 2.1232          | 0.0037                 |
| 0.2938        | 52.9874 | 3166 | 2.1076          | 0.0037                 |
| 0.289         | 53.9916 | 3226 | 2.0892          | 0.0037                 |
| 0.3044        | 54.9958 | 3286 | 2.0692          | 0.0037                 |
| 0.277         | 56.0    | 3346 | 2.0667          | 0.0037                 |
| 0.2774        | 56.9874 | 3405 | 2.0554          | 0.0037                 |
| 0.2717        | 57.9916 | 3465 | 2.0369          | 0.0037                 |
| 0.2722        | 58.9958 | 3525 | 2.0261          | 0.0037                 |
| 0.2325        | 60.0    | 3585 | 2.0419          | 0.0037                 |
| 0.2387        | 60.9874 | 3644 | 2.0073          | 0.0037                 |
| 0.2343        | 61.9916 | 3704 | 2.0230          | 0.0037                 |
| 0.2281        | 62.9958 | 3764 | 2.0228          | 0.0037                 |
| 0.2597        | 64.0    | 3824 | 1.9956          | 0.0037                 |
| 0.223         | 64.9874 | 3883 | 1.9902          | 0.0037                 |
| 0.2213        | 65.9916 | 3943 | 1.9778          | 0.0037                 |
| 0.1835        | 66.9958 | 4003 | 1.9945          | 0.0037                 |
| 0.2247        | 68.0    | 4063 | 1.9703          | 0.0037                 |
| 0.1819        | 68.9874 | 4122 | 1.9623          | 0.0037                 |
| 0.2096        | 69.9916 | 4182 | 1.9686          | 0.0037                 |
| 0.186         | 70.9958 | 4242 | 1.9764          | 0.0037                 |
| 0.1956        | 72.0    | 4302 | 1.9606          | 0.0037                 |
| 0.197         | 72.9874 | 4361 | 1.9432          | 0.0037                 |
| 0.1867        | 73.9916 | 4421 | 1.9461          | 0.0037                 |
| 0.1994        | 74.9958 | 4481 | 1.9547          | 0.0037                 |
| 0.1631        | 76.0    | 4541 | 1.9373          | 0.0037                 |
| 0.184         | 76.9874 | 4600 | 1.9329          | 0.0037                 |
| 0.1518        | 77.9916 | 4660 | 1.9355          | 0.0037                 |
| 0.1774        | 78.9958 | 4720 | 1.9367          | 0.0037                 |
| 0.1558        | 80.0    | 4780 | 1.9211          | 0.0037                 |
| 0.1859        | 80.9874 | 4839 | 1.9256          | 0.0037                 |
| 0.1673        | 81.9916 | 4899 | 1.9271          | 0.0037                 |
| 0.1531        | 82.9958 | 4959 | 1.9332          | 0.0037                 |
| 0.1763        | 84.0    | 5019 | 1.9154          | 0.0037                 |
| 0.1594        | 84.9874 | 5078 | 1.9143          | 0.0037                 |
| 0.17          | 85.9916 | 5138 | 1.9098          | 0.0037                 |
| 0.1246        | 86.9958 | 5198 | 1.9123          | 0.0037                 |
| 0.1699        | 88.0    | 5258 | 1.9066          | 0.0037                 |
| 0.1627        | 88.9874 | 5317 | 1.9054          | 0.0037                 |
| 0.1663        | 89.9916 | 5377 | 1.9040          | 0.0037                 |
| 0.1349        | 90.9958 | 5437 | 1.9031          | 0.0037                 |
| 0.1578        | 92.0    | 5497 | 1.9065          | 0.0037                 |
| 0.1553        | 92.9874 | 5556 | 1.8997          | 0.0037                 |
| 0.1393        | 93.9916 | 5616 | 1.8972          | 0.0037                 |
| 0.1652        | 94.9958 | 5676 | 1.8960          | 0.0037                 |
| 0.1677        | 96.0    | 5736 | 1.9002          | 0.0037                 |
| 0.1544        | 96.9874 | 5795 | 1.8966          | 0.0037                 |
| 0.1359        | 97.9916 | 5855 | 1.8966          | 0.0037                 |
| 0.1495        | 98.7448 | 5900 | 1.8965          | 0.0037                 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1

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