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---
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: VIT-food101-image-classifier
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.933
---
<!-- 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-food101-image-classifier
This model was trained from scratch on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5661
- Accuracy: 0.933
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1716 | 0.99 | 62 | 1.2149 | 0.896 |
| 0.7758 | 1.99 | 124 | 0.8727 | 0.906 |
| 0.6269 | 2.99 | 186 | 0.6833 | 0.928 |
| 0.5495 | 3.99 | 248 | 0.6041 | 0.931 |
| 0.4973 | 4.99 | 310 | 0.5661 | 0.933 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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