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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-flowers102
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-flowers102
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 nelorth/oxford-flowers dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0770
- Accuracy: 0.9853
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5779 | 0.22 | 100 | 2.8895 | 0.7775 |
| 1.2226 | 0.45 | 200 | 1.5942 | 0.9255 |
| 0.606 | 0.67 | 300 | 0.8012 | 0.9529 |
| 0.3413 | 0.89 | 400 | 0.4845 | 0.9706 |
| 0.1571 | 1.11 | 500 | 0.2611 | 0.9814 |
| 0.1237 | 1.34 | 600 | 0.1691 | 0.9784 |
| 0.049 | 1.56 | 700 | 0.1146 | 0.9892 |
| 0.0763 | 1.78 | 800 | 0.1209 | 0.9863 |
| 0.0864 | 2.0 | 900 | 0.1223 | 0.9804 |
| 0.0786 | 2.23 | 1000 | 0.1075 | 0.9833 |
| 0.0269 | 2.45 | 1100 | 0.0919 | 0.9843 |
| 0.0178 | 2.67 | 1200 | 0.0795 | 0.9873 |
| 0.0165 | 2.9 | 1300 | 0.0727 | 0.9873 |
| 0.0144 | 3.12 | 1400 | 0.0784 | 0.9853 |
| 0.0138 | 3.34 | 1500 | 0.0759 | 0.9853 |
| 0.0135 | 3.56 | 1600 | 0.0737 | 0.9863 |
| 0.0123 | 3.79 | 1700 | 0.0770 | 0.9853 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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