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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: finetuned-indian-food
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. -->
# finetuned-indian-food
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: 0.0027
- Accuracy: 0.9996
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7056 | 0.1 | 100 | 0.5113 | 0.8881 |
| 0.3027 | 0.21 | 200 | 0.1280 | 0.9796 |
| 0.2823 | 0.31 | 300 | 0.1580 | 0.9656 |
| 0.3273 | 0.42 | 400 | 0.0879 | 0.9837 |
| 0.1808 | 0.52 | 500 | 0.0812 | 0.9822 |
| 0.2101 | 0.63 | 600 | 0.0339 | 0.9937 |
| 0.1495 | 0.73 | 700 | 0.0568 | 0.9833 |
| 0.1296 | 0.84 | 800 | 0.0629 | 0.9844 |
| 0.1462 | 0.94 | 900 | 0.0886 | 0.9733 |
| 0.0519 | 1.04 | 1000 | 0.0544 | 0.9870 |
| 0.3192 | 1.15 | 1100 | 0.0892 | 0.9726 |
| 0.158 | 1.25 | 1200 | 0.0632 | 0.98 |
| 0.0266 | 1.36 | 1300 | 0.0233 | 0.9944 |
| 0.1832 | 1.46 | 1400 | 0.0292 | 0.9930 |
| 0.1212 | 1.57 | 1500 | 0.0489 | 0.9852 |
| 0.0994 | 1.67 | 1600 | 0.0142 | 0.9974 |
| 0.0219 | 1.78 | 1700 | 0.0277 | 0.9930 |
| 0.0664 | 1.88 | 1800 | 0.0158 | 0.9974 |
| 0.0834 | 1.99 | 1900 | 0.0124 | 0.9978 |
| 0.1093 | 2.09 | 2000 | 0.0140 | 0.9974 |
| 0.1726 | 2.19 | 2100 | 0.0147 | 0.9963 |
| 0.0476 | 2.3 | 2200 | 0.0058 | 0.9993 |
| 0.0257 | 2.4 | 2300 | 0.0424 | 0.9911 |
| 0.0215 | 2.51 | 2400 | 0.0076 | 0.9989 |
| 0.0748 | 2.61 | 2500 | 0.0099 | 0.9974 |
| 0.0059 | 2.72 | 2600 | 0.0053 | 0.9993 |
| 0.0527 | 2.82 | 2700 | 0.0149 | 0.9963 |
| 0.0203 | 2.93 | 2800 | 0.0041 | 0.9993 |
| 0.0791 | 3.03 | 2900 | 0.0033 | 0.9989 |
| 0.0389 | 3.13 | 3000 | 0.0033 | 0.9989 |
| 0.0459 | 3.24 | 3100 | 0.0044 | 0.9989 |
| 0.0276 | 3.34 | 3200 | 0.0031 | 0.9996 |
| 0.0139 | 3.45 | 3300 | 0.0028 | 0.9996 |
| 0.0076 | 3.55 | 3400 | 0.0055 | 0.9985 |
| 0.0097 | 3.66 | 3500 | 0.0027 | 0.9996 |
| 0.0193 | 3.76 | 3600 | 0.0026 | 0.9996 |
| 0.0471 | 3.87 | 3700 | 0.0027 | 0.9996 |
| 0.0282 | 3.97 | 3800 | 0.0027 | 0.9996 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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