
<!-- 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 food101 dataset.

## 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

### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["food101"], "model-index": [{"name": "my_awesome_food_model", "results": []}]}
transformers pytorch vit image-classification generated_from_trainer dataset:food101 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-food101 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# my_awesome_food_model

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the food101 dataset.

## 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

### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2

# my_awesome_food_model

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the food101 dataset. ## 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 ### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-food101 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # my_awesome_food_model

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the food101 dataset. ## 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 ### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2