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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-fake-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-fake-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 the indian_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4855
- Accuracy: 0.8548
## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6061 | 1.0 | 176 | 0.5937 | 0.6855 |
| 0.481 | 2.0 | 352 | 0.5138 | 0.8226 |
| 0.5522 | 3.0 | 528 | 0.4973 | 0.8065 |
| 0.4092 | 4.0 | 704 | 0.5557 | 0.7903 |
| 0.4882 | 5.0 | 880 | 0.4998 | 0.7984 |
| 0.4442 | 6.0 | 1056 | 0.4647 | 0.8387 |
| 0.5749 | 7.0 | 1232 | 0.4464 | 0.8306 |
| 0.4529 | 8.0 | 1408 | 0.5366 | 0.8065 |
| 0.5287 | 9.0 | 1584 | 0.4633 | 0.8387 |
| 0.3821 | 10.0 | 1760 | 0.4983 | 0.8387 |
| 0.2409 | 11.0 | 1936 | 0.4855 | 0.8548 |
| 0.2025 | 12.0 | 2112 | 0.5102 | 0.8387 |
| 0.2045 | 13.0 | 2288 | 0.4942 | 0.8387 |
| 0.4097 | 14.0 | 2464 | 0.4954 | 0.8387 |
| 0.5798 | 15.0 | 2640 | 0.4941 | 0.8387 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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