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# vit-base-codenames

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 codenames-pictures dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7722
- Accuracy: 0.4643

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5428        | 0.16  | 100  | 2.4585          | 0.4106   |
| 1.498         | 0.31  | 200  | 1.5259          | 0.4449   |
| 1.2131        | 0.47  | 300  | 1.1431          | 0.4652   |
| 1.0505        | 0.63  | 400  | 1.0880          | 0.4485   |
| 0.9738        | 0.78  | 500  | 1.0141          | 0.4599   |
| 1.0137        | 0.94  | 600  | 0.9250          | 0.4670   |
| 0.932         | 1.1   | 700  | 0.9285          | 0.4731   |
| 0.9456        | 1.25  | 800  | 0.8803          | 0.4678   |
| 0.7922        | 1.41  | 900  | 0.8776          | 0.5084   |
| 0.8335        | 1.56  | 1000 | 0.8627          | 0.4811   |
| 0.7652        | 1.72  | 1100 | 0.8779          | 0.4952   |
| 0.8312        | 1.88  | 1200 | 0.8086          | 0.4872   |
| 0.7136        | 2.03  | 1300 | 0.8158          | 0.4590   |
| 0.7662        | 2.19  | 1400 | 0.8138          | 0.4661   |
| 0.7175        | 2.35  | 1500 | 0.8079          | 0.4775   |
| 0.7614        | 2.5   | 1600 | 0.8031          | 0.4802   |
| 0.7665        | 2.66  | 1700 | 0.8083          | 0.4467   |
| 0.7557        | 2.82  | 1800 | 0.8003          | 0.4819   |
| 0.7678        | 2.97  | 1900 | 0.8159          | 0.5013   |
| 0.697         | 3.13  | 2000 | 0.7845          | 0.4855   |
| 0.7211        | 3.29  | 2100 | 0.7942          | 0.4670   |
| 0.7307        | 3.44  | 2200 | 0.7908          | 0.4740   |
| 0.7122        | 3.6   | 2300 | 0.7920          | 0.4502   |
| 0.7597        | 3.76  | 2400 | 0.7722          | 0.4643   |
| 0.7165        | 3.91  | 2500 | 0.7784          | 0.4819   |
| 0.7238        | 4.07  | 2600 | 0.7896          | 0.4687   |
| 0.7047        | 4.23  | 2700 | 0.7849          | 0.4617   |
| 0.7122        | 4.38  | 2800 | 0.7963          | 0.4881   |
| 0.7574        | 4.54  | 2900 | 0.8179          | 0.4934   |
| 0.7987        | 4.69  | 3000 | 0.8060          | 0.4344   |
| 0.7517        | 4.85  | 3100 | 0.8018          | 0.4537   |
| 0.7402        | 5.01  | 3200 | 0.8076          | 0.4784   |
| 0.7223        | 5.16  | 3300 | 0.8026          | 0.4405   |
| 0.7417        | 5.32  | 3400 | 0.8005          | 0.4185   |
| 0.7236        | 5.48  | 3500 | 0.7964          | 0.4238   |
| 0.7114        | 5.63  | 3600 | 0.7989          | 0.3991   |
| 0.7237        | 5.79  | 3700 | 0.8229          | 0.4070   |
| 0.7203        | 5.95  | 3800 | 0.7989          | 0.4740   |
| 0.7081        | 6.1   | 3900 | 0.8194          | 0.4211   |
| 0.6839        | 6.26  | 4000 | 0.8013          | 0.4300   |
| 0.6832        | 6.42  | 4100 | 0.8065          | 0.3789   |
| 0.7093        | 6.57  | 4200 | 0.8014          | 0.3930   |
| 0.7037        | 6.73  | 4300 | 0.8170          | 0.4185   |
| 0.6999        | 6.89  | 4400 | 0.8318          | 0.4123   |
| 0.7166        | 7.04  | 4500 | 0.8254          | 0.4256   |
| 0.6838        | 7.2   | 4600 | 0.8204          | 0.3903   |
| 0.7135        | 7.36  | 4700 | 0.8385          | 0.3612   |
| 0.6999        | 7.51  | 4800 | 0.8406          | 0.3568   |
| 0.7007        | 7.67  | 4900 | 0.8353          | 0.3674   |
| 0.6892        | 7.82  | 5000 | 0.8318          | 0.3339   |
| 0.7036        | 7.98  | 5100 | 0.8310          | 0.3198   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.1
- Datasets 2.12.0
- Tokenizers 0.15.1

astronaut banjo cigarette crown desert egg fire flamingo fox fruit giraffe glasses bear hat headphones horse house kangaroo koala lion man mermaid microphone bicycle parachute penguin piano pig pirate rabbit radio rat robot saxophone boat skateboard squirrel superhero tennis unicorn weightlifting wizard book boxing burglar cat chef