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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-cifar100-cifar100
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-cifar100-cifar100
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 cifar100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2851
- Accuracy: 0.9252
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0634 | 1.0 | 5313 | 0.7235 | 0.8777 |
| 0.7839 | 2.0 | 10626 | 0.3731 | 0.9056 |
| 0.5749 | 3.0 | 15939 | 0.3214 | 0.9153 |
| 0.3432 | 4.0 | 21252 | 0.2990 | 0.9209 |
| 0.4763 | 5.0 | 26565 | 0.2851 | 0.9252 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.0.1+cu117
- Datasets 3.0.0
- Tokenizers 0.19.1
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