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# vit-snacks

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 Matthijs/snacks dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2754
- Accuracy: 0.9393

## Model description

upload any image of your fave yummy snack 

## Intended uses & limitations

there are only 20 different varieties of snacks

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8724        | 0.33  | 100  | 0.9118          | 0.8670   |
| 0.5628        | 0.66  | 200  | 0.6873          | 0.8471   |
| 0.4421        | 0.99  | 300  | 0.4995          | 0.8691   |
| 0.2837        | 1.32  | 400  | 0.4008          | 0.9026   |
| 0.1645        | 1.65  | 500  | 0.3702          | 0.9058   |
| 0.1604        | 1.98  | 600  | 0.3981          | 0.8921   |
| 0.0498        | 2.31  | 700  | 0.3185          | 0.9204   |
| 0.0406        | 2.64  | 800  | 0.3427          | 0.9141   |
| 0.1049        | 2.97  | 900  | 0.3444          | 0.9173   |
| 0.0272        | 3.3   | 1000 | 0.3168          | 0.9246   |
| 0.0186        | 3.63  | 1100 | 0.3142          | 0.9288   |
| 0.0203        | 3.96  | 1200 | 0.2931          | 0.9298   |
| 0.007         | 4.29  | 1300 | 0.2754          | 0.9393   |
| 0.0072        | 4.62  | 1400 | 0.2778          | 0.9403   |
| 0.0073        | 4.95  | 1500 | 0.2782          | 0.9393   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["snacks"], "metrics": ["accuracy"], "model-index": [{"name": "vit-snacks", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "Matthijs/snacks", "type": "snacks", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9392670157068063, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:snacks license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-snacks #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

vit-snacks
==========


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Matthijs/snacks dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2754
* Accuracy: 0.9393


Model description
-----------------


upload any image of your fave yummy snack


Intended uses & limitations
---------------------------


there are only 20 different varieties of snacks


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-snacks #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-snacks #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1