
<!-- 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. -->

# my_awesome_food_model

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9108
- Accuracy: 0.8405

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4869        | 1.0   | 125  | 1.4082          | 0.754    |
| 1.0009        | 2.0   | 250  | 1.0099          | 0.823    |
| 0.8853        | 3.0   | 375  | 0.9108          | 0.8405   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

dam parking lot sparse shrub land works oil field meadow ground track field detached house golf course forest desert lake greenhouse beach paddy field ice land bare land storage tank basketball court island substation mobile home park cemetery pier quarry solar power plant helipad roundabout runway wastewater plant river apartment dry field intersection bridge swimming pool commercial area church road orchard terraced field stadium train station railway viaduct mine wind turbine rock land baseball field apron tennis court