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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: banknote18k
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. -->
# banknote18k
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: 0.0096
- Accuracy: 0.9987
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4947 | 0.12 | 100 | 0.3407 | 0.9451 |
| 0.423 | 0.23 | 200 | 0.2200 | 0.9451 |
| 0.2237 | 0.35 | 300 | 0.1613 | 0.9536 |
| 0.2806 | 0.46 | 400 | 0.0884 | 0.9810 |
| 0.1188 | 0.58 | 500 | 0.0512 | 0.9895 |
| 0.3279 | 0.7 | 600 | 0.0568 | 0.9876 |
| 0.1054 | 0.81 | 700 | 0.0342 | 0.9928 |
| 0.0924 | 0.93 | 800 | 0.0536 | 0.9863 |
| 0.1068 | 1.05 | 900 | 0.0746 | 0.9804 |
| 0.213 | 1.16 | 1000 | 0.0340 | 0.9948 |
| 0.159 | 1.28 | 1100 | 0.0426 | 0.9882 |
| 0.1048 | 1.39 | 1200 | 0.0248 | 0.9948 |
| 0.1493 | 1.51 | 1300 | 0.0154 | 0.9974 |
| 0.1274 | 1.63 | 1400 | 0.0394 | 0.9922 |
| 0.0915 | 1.74 | 1500 | 0.0422 | 0.9882 |
| 0.0598 | 1.86 | 1600 | 0.0219 | 0.9948 |
| 0.1241 | 1.97 | 1700 | 0.0173 | 0.9948 |
| 0.1249 | 2.09 | 1800 | 0.0179 | 0.9954 |
| 0.0131 | 2.21 | 1900 | 0.0124 | 0.9961 |
| 0.0392 | 2.32 | 2000 | 0.0123 | 0.9967 |
| 0.0655 | 2.44 | 2100 | 0.0223 | 0.9948 |
| 0.0355 | 2.56 | 2200 | 0.0256 | 0.9941 |
| 0.0335 | 2.67 | 2300 | 0.0147 | 0.9967 |
| 0.0618 | 2.79 | 2400 | 0.0123 | 0.9974 |
| 0.0476 | 2.9 | 2500 | 0.0110 | 0.9980 |
| 0.0452 | 3.02 | 2600 | 0.0192 | 0.9967 |
| 0.0104 | 3.14 | 2700 | 0.0184 | 0.9967 |
| 0.036 | 3.25 | 2800 | 0.0122 | 0.9974 |
| 0.0358 | 3.37 | 2900 | 0.0104 | 0.9987 |
| 0.054 | 3.48 | 3000 | 0.0101 | 0.9987 |
| 0.0395 | 3.6 | 3100 | 0.0132 | 0.9967 |
| 0.0367 | 3.72 | 3200 | 0.0096 | 0.9987 |
| 0.0261 | 3.83 | 3300 | 0.0101 | 0.9980 |
| 0.0017 | 3.95 | 3400 | 0.0096 | 0.9987 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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