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# vit-base-patch16-224-finetuned-on-all-affectnet_short

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0520
- Accuracy: 0.7259
- Precision: 0.7293
- Recall: 0.7259
- F1: 0.7255

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 14
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.5399        | 1.0   | 91   | 1.4698          | 0.5097   | 0.5079    | 0.5097 | 0.4827 |
| 1.284         | 2.0   | 182  | 1.2026          | 0.6409   | 0.6514    | 0.6409 | 0.6226 |
| 1.2259        | 3.0   | 273  | 1.1367          | 0.6722   | 0.6749    | 0.6722 | 0.6694 |
| 1.1663        | 4.0   | 364  | 1.1086          | 0.6838   | 0.6903    | 0.6838 | 0.6814 |
| 1.1401        | 5.0   | 455  | 1.0782          | 0.7055   | 0.7070    | 0.7055 | 0.7042 |
| 1.1229        | 6.0   | 546  | 1.0734          | 0.7055   | 0.7093    | 0.7055 | 0.7036 |
| 1.0929        | 7.0   | 637  | 1.0674          | 0.7120   | 0.7147    | 0.7120 | 0.7099 |
| 1.0826        | 8.0   | 728  | 1.0601          | 0.7210   | 0.7226    | 0.7210 | 0.7191 |
| 1.0414        | 9.0   | 819  | 1.0558          | 0.7203   | 0.7211    | 0.7203 | 0.7196 |
| 1.0649        | 10.0  | 910  | 1.0499          | 0.7179   | 0.7194    | 0.7179 | 0.7175 |
| 1.0554        | 11.0  | 1001 | 1.0520          | 0.7259   | 0.7293    | 0.7259 | 0.7255 |
| 1.0496        | 12.0  | 1092 | 1.0466          | 0.7210   | 0.7212    | 0.7210 | 0.7204 |
| 1.064         | 13.0  | 1183 | 1.0502          | 0.7220   | 0.7235    | 0.7220 | 0.7211 |
| 1.0386        | 14.0  | 1274 | 1.0475          | 0.7206   | 0.7208    | 0.7206 | 0.7200 |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

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