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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-food101-24-12
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9068514851485149
---
<!-- 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-patch16-224-in21k-food101-24-12
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 food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3533
- Accuracy: 0.9069
## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7927 | 1.0 | 789 | 2.5629 | 0.7693 |
| 1.256 | 2.0 | 1578 | 0.9637 | 0.8583 |
| 0.94 | 3.0 | 2367 | 0.5866 | 0.8816 |
| 0.6693 | 4.0 | 3157 | 0.4752 | 0.8888 |
| 0.6337 | 5.0 | 3946 | 0.4282 | 0.8941 |
| 0.5811 | 6.0 | 4735 | 0.4110 | 0.8949 |
| 0.4661 | 7.0 | 5524 | 0.3875 | 0.8990 |
| 0.4188 | 8.0 | 6314 | 0.3776 | 0.9010 |
| 0.5045 | 9.0 | 7103 | 0.3633 | 0.9049 |
| 0.3437 | 10.0 | 7892 | 0.3611 | 0.9058 |
| 0.3494 | 11.0 | 8681 | 0.3568 | 0.9060 |
| 0.3381 | 12.0 | 9468 | 0.3533 | 0.9069 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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