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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
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: 0.2293
- Accuracy: 0.9405
- Precision: 0.9395
- Recall: 0.9420
- F1: 0.9402
## 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 | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8589 | 0.3 | 100 | 0.5618 | 0.8714 | 0.8981 | 0.8620 | 0.8696 |
| 0.6973 | 0.6 | 200 | 0.5544 | 0.8608 | 0.8742 | 0.8690 | 0.8630 |
| 0.4078 | 0.9 | 300 | 0.4671 | 0.8831 | 0.8915 | 0.8840 | 0.8812 |
| 0.3818 | 1.2 | 400 | 0.4203 | 0.8884 | 0.9017 | 0.8864 | 0.8877 |
| 0.2262 | 1.5 | 500 | 0.3481 | 0.9107 | 0.9177 | 0.9085 | 0.9098 |
| 0.2137 | 1.8 | 600 | 0.3761 | 0.9022 | 0.9094 | 0.9027 | 0.9026 |
| 0.4515 | 2.1 | 700 | 0.3722 | 0.9044 | 0.9091 | 0.9041 | 0.9017 |
| 0.3024 | 2.4 | 800 | 0.3105 | 0.9203 | 0.9198 | 0.9220 | 0.9188 |
| 0.1748 | 2.7 | 900 | 0.2767 | 0.9288 | 0.9274 | 0.9293 | 0.9272 |
| 0.1959 | 3.0 | 1000 | 0.2825 | 0.9256 | 0.9318 | 0.9243 | 0.9230 |
| 0.1663 | 3.3 | 1100 | 0.2549 | 0.9341 | 0.9362 | 0.9366 | 0.9356 |
| 0.0513 | 3.6 | 1200 | 0.2254 | 0.9416 | 0.9436 | 0.9422 | 0.9424 |
| 0.1478 | 3.9 | 1300 | 0.2293 | 0.9405 | 0.9395 | 0.9420 | 0.9402 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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