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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9360791655522868
---
<!-- 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
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