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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-cifar10
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-cifar10
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: 0.0803
- Accuracy: 0.9773
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1043 | 0.0457 | 100 | 0.2855 | 0.919 |
| 0.2671 | 0.0914 | 200 | 0.3650 | 0.9015 |
| 0.2935 | 0.1371 | 300 | 0.3167 | 0.9067 |
| 0.27 | 0.1828 | 400 | 0.3518 | 0.8922 |
| 0.3634 | 0.2285 | 500 | 0.3660 | 0.8953 |
| 0.2559 | 0.2742 | 600 | 0.3964 | 0.8901 |
| 0.197 | 0.3199 | 700 | 0.2481 | 0.9253 |
| 0.2594 | 0.3656 | 800 | 0.2486 | 0.923 |
| 0.4545 | 0.4113 | 900 | 0.3271 | 0.9 |
| 0.1243 | 0.4570 | 1000 | 0.2448 | 0.9269 |
| 0.3593 | 0.5027 | 1100 | 0.2118 | 0.9354 |
| 0.1375 | 0.5484 | 1200 | 0.2205 | 0.9349 |
| 0.1521 | 0.5941 | 1300 | 0.2009 | 0.9376 |
| 0.1237 | 0.6399 | 1400 | 0.1803 | 0.9445 |
| 0.2214 | 0.6856 | 1500 | 0.2026 | 0.9395 |
| 0.1324 | 0.7313 | 1600 | 0.1635 | 0.9493 |
| 0.1864 | 0.7770 | 1700 | 0.1672 | 0.9493 |
| 0.128 | 0.8227 | 1800 | 0.2015 | 0.9409 |
| 0.121 | 0.8684 | 1900 | 0.1753 | 0.9451 |
| 0.1918 | 0.9141 | 2000 | 0.1370 | 0.9588 |
| 0.1658 | 0.9598 | 2100 | 0.1543 | 0.9535 |
| 0.1088 | 1.0055 | 2200 | 0.1361 | 0.9577 |
| 0.0916 | 1.0512 | 2300 | 0.1393 | 0.9597 |
| 0.005 | 1.0969 | 2400 | 0.1295 | 0.9621 |
| 0.0294 | 1.1426 | 2500 | 0.1327 | 0.9639 |
| 0.0939 | 1.1883 | 2600 | 0.1409 | 0.9621 |
| 0.0756 | 1.2340 | 2700 | 0.1202 | 0.9682 |
| 0.0466 | 1.2797 | 2800 | 0.1274 | 0.964 |
| 0.0565 | 1.3254 | 2900 | 0.1250 | 0.9663 |
| 0.0609 | 1.3711 | 3000 | 0.1299 | 0.9657 |
| 0.0201 | 1.4168 | 3100 | 0.1203 | 0.9685 |
| 0.0258 | 1.4625 | 3200 | 0.1166 | 0.9693 |
| 0.0913 | 1.5082 | 3300 | 0.1009 | 0.9736 |
| 0.0235 | 1.5539 | 3400 | 0.0964 | 0.9732 |
| 0.0089 | 1.5996 | 3500 | 0.0966 | 0.9747 |
| 0.0455 | 1.6453 | 3600 | 0.0963 | 0.9748 |
| 0.0271 | 1.6910 | 3700 | 0.0874 | 0.9763 |
| 0.0407 | 1.7367 | 3800 | 0.0898 | 0.9761 |
| 0.1095 | 1.7824 | 3900 | 0.0849 | 0.976 |
| 0.0327 | 1.8282 | 4000 | 0.0926 | 0.9745 |
| 0.0427 | 1.8739 | 4100 | 0.0811 | 0.9769 |
| 0.003 | 1.9196 | 4200 | 0.0821 | 0.9761 |
| 0.0182 | 1.9653 | 4300 | 0.0803 | 0.9773 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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