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---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-clothing-leafs-example
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-clothing-leafs-example
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1420
- Accuracy: 0.0448
## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 8.6059 | 0.14 | 1000 | 8.5844 | 0.0002 |
| 8.5506 | 0.28 | 2000 | 8.5189 | 0.0010 |
| 8.4931 | 0.41 | 3000 | 8.4641 | 0.0012 |
| 8.4223 | 0.55 | 4000 | 8.3495 | 0.0016 |
| 8.3144 | 0.69 | 5000 | 8.2552 | 0.0021 |
| 8.1936 | 0.83 | 6000 | 8.1385 | 0.0024 |
| 8.0638 | 0.97 | 7000 | 7.9924 | 0.0028 |
| 7.8485 | 1.11 | 8000 | 7.8366 | 0.0036 |
| 7.6933 | 1.24 | 9000 | 7.6595 | 0.0045 |
| 7.5808 | 1.38 | 10000 | 7.5232 | 0.0062 |
| 7.4352 | 1.52 | 11000 | 7.3816 | 0.0070 |
| 7.3279 | 1.66 | 12000 | 7.2853 | 0.0084 |
| 7.2141 | 1.8 | 13000 | 7.1553 | 0.0105 |
| 7.151 | 1.94 | 14000 | 7.0853 | 0.0119 |
| 6.9695 | 2.07 | 15000 | 7.0088 | 0.0134 |
| 6.8563 | 2.21 | 16000 | 6.9409 | 0.0139 |
| 6.8019 | 2.35 | 17000 | 6.8634 | 0.0158 |
| 6.7372 | 2.49 | 18000 | 6.8001 | 0.0175 |
| 6.6903 | 2.63 | 19000 | 6.7323 | 0.0191 |
| 6.6482 | 2.77 | 20000 | 6.6638 | 0.0207 |
| 6.5669 | 2.9 | 21000 | 6.6090 | 0.0239 |
| 6.4484 | 3.04 | 22000 | 6.5441 | 0.0240 |
| 6.2568 | 3.18 | 23000 | 6.5015 | 0.0273 |
| 6.2452 | 3.32 | 24000 | 6.4589 | 0.0304 |
| 6.2002 | 3.46 | 25000 | 6.4312 | 0.0310 |
| 6.1699 | 3.6 | 26000 | 6.3723 | 0.0319 |
| 6.1284 | 3.73 | 27000 | 6.3324 | 0.0343 |
| 6.1186 | 3.87 | 28000 | 6.3029 | 0.0350 |
| 6.0611 | 4.01 | 29000 | 6.2723 | 0.0381 |
| 5.7883 | 4.15 | 30000 | 6.2527 | 0.0383 |
| 5.7684 | 4.29 | 31000 | 6.2186 | 0.0392 |
| 5.7701 | 4.43 | 32000 | 6.2031 | 0.0403 |
| 5.7473 | 4.56 | 33000 | 6.1777 | 0.0430 |
| 5.735 | 4.7 | 34000 | 6.1634 | 0.0442 |
| 5.7324 | 4.84 | 35000 | 6.1494 | 0.0443 |
| 5.6949 | 4.98 | 36000 | 6.1420 | 0.0448 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
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