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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: ViTForImageClassification
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. -->
# ViTForImageClassification
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 [CIFAR10](https://huggingface.co/datasets/Andron00e/CIFAR10-custom) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1199
- Accuracy: 0.9678
## Model description
[A detailed description of model architecture can be found here](https://github.com/huggingface/transformers/blob/main/src/transformers/models/vit/modeling_vit.py#L756)
## Training and evaluation data
[CIFAR10](https://huggingface.co/datasets/Andron00e/CIFAR10-custom)
## Training procedure
Straightforward tuning of all model's parameters.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2995 | 0.27 | 100 | 0.3419 | 0.9108 |
| 0.2289 | 0.53 | 200 | 0.2482 | 0.9288 |
| 0.1811 | 0.8 | 300 | 0.2139 | 0.9357 |
| 0.0797 | 1.07 | 400 | 0.1813 | 0.946 |
| 0.1128 | 1.33 | 500 | 0.1741 | 0.9452 |
| 0.086 | 1.6 | 600 | 0.1659 | 0.9513 |
| 0.0815 | 1.87 | 700 | 0.1468 | 0.9547 |
| 0.048 | 2.13 | 800 | 0.1393 | 0.9592 |
| 0.021 | 2.4 | 900 | 0.1399 | 0.9603 |
| 0.0271 | 2.67 | 1000 | 0.1334 | 0.9642 |
| 0.0231 | 2.93 | 1100 | 0.1228 | 0.9658 |
| 0.0101 | 3.2 | 1200 | 0.1229 | 0.9673 |
| 0.0041 | 3.47 | 1300 | 0.1189 | 0.9675 |
| 0.0043 | 3.73 | 1400 | 0.1165 | 0.9683 |
| 0.0067 | 4.0 | 1500 | 0.1145 | 0.9697 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.14.1
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