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# Fer_vit_jaffe_crop_GOOGLE_0

This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4370
- Accuracy: 0.9

## 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: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 1    | 2.4680          | 0.1333   |
| No log        | 2.0   | 2    | 2.2791          | 0.2333   |
| No log        | 3.0   | 3    | 2.2505          | 0.1667   |
| No log        | 4.0   | 4    | 2.0650          | 0.1      |
| No log        | 5.0   | 5    | 2.1205          | 0.0333   |
| No log        | 6.0   | 6    | 2.0198          | 0.1      |
| No log        | 7.0   | 7    | 2.0317          | 0.1333   |
| No log        | 8.0   | 8    | 1.9863          | 0.2333   |
| No log        | 9.0   | 9    | 1.9390          | 0.3      |
| 2.1093        | 10.0  | 10   | 1.8465          | 0.2667   |
| 2.1093        | 11.0  | 11   | 1.6948          | 0.4333   |
| 2.1093        | 12.0  | 12   | 1.6453          | 0.4333   |
| 2.1093        | 13.0  | 13   | 1.6213          | 0.3333   |
| 2.1093        | 14.0  | 14   | 1.6045          | 0.3667   |
| 2.1093        | 15.0  | 15   | 1.5593          | 0.4333   |
| 2.1093        | 16.0  | 16   | 1.5160          | 0.5      |
| 2.1093        | 17.0  | 17   | 1.5322          | 0.4667   |
| 2.1093        | 18.0  | 18   | 1.4750          | 0.5      |
| 2.1093        | 19.0  | 19   | 1.3553          | 0.5333   |
| 1.3827        | 20.0  | 20   | 1.2704          | 0.4667   |
| 1.3827        | 21.0  | 21   | 1.2823          | 0.4667   |
| 1.3827        | 22.0  | 22   | 1.3789          | 0.5333   |
| 1.3827        | 23.0  | 23   | 1.2368          | 0.5667   |
| 1.3827        | 24.0  | 24   | 1.0561          | 0.6      |
| 1.3827        | 25.0  | 25   | 1.2039          | 0.5333   |
| 1.3827        | 26.0  | 26   | 1.2061          | 0.5333   |
| 1.3827        | 27.0  | 27   | 0.9144          | 0.6333   |
| 1.3827        | 28.0  | 28   | 1.0374          | 0.6      |
| 1.3827        | 29.0  | 29   | 1.0670          | 0.6333   |
| 0.6174        | 30.0  | 30   | 1.0691          | 0.6667   |
| 0.6174        | 31.0  | 31   | 0.9445          | 0.6667   |
| 0.6174        | 32.0  | 32   | 0.8885          | 0.5667   |
| 0.6174        | 33.0  | 33   | 0.9647          | 0.6      |
| 0.6174        | 34.0  | 34   | 1.0187          | 0.5667   |
| 0.6174        | 35.0  | 35   | 0.9037          | 0.6333   |
| 0.6174        | 36.0  | 36   | 0.9069          | 0.6      |
| 0.6174        | 37.0  | 37   | 0.8999          | 0.6333   |
| 0.6174        | 38.0  | 38   | 0.6198          | 0.7667   |
| 0.6174        | 39.0  | 39   | 0.8034          | 0.6667   |
| 0.2248        | 40.0  | 40   | 0.9049          | 0.6667   |
| 0.2248        | 41.0  | 41   | 0.7231          | 0.6667   |
| 0.2248        | 42.0  | 42   | 0.6554          | 0.7      |
| 0.2248        | 43.0  | 43   | 0.6591          | 0.8      |
| 0.2248        | 44.0  | 44   | 0.7196          | 0.8      |
| 0.2248        | 45.0  | 45   | 0.7233          | 0.7      |
| 0.2248        | 46.0  | 46   | 0.6112          | 0.8      |
| 0.2248        | 47.0  | 47   | 0.4299          | 0.8667   |
| 0.2248        | 48.0  | 48   | 0.5479          | 0.8      |
| 0.2248        | 49.0  | 49   | 0.5996          | 0.8333   |
| 0.0773        | 50.0  | 50   | 0.6714          | 0.7333   |
| 0.0773        | 51.0  | 51   | 0.4989          | 0.8333   |
| 0.0773        | 52.0  | 52   | 0.4956          | 0.8667   |
| 0.0773        | 53.0  | 53   | 0.4367          | 0.8333   |
| 0.0773        | 54.0  | 54   | 0.4542          | 0.8333   |
| 0.0773        | 55.0  | 55   | 0.5991          | 0.8      |
| 0.0773        | 56.0  | 56   | 0.6906          | 0.7667   |
| 0.0773        | 57.0  | 57   | 0.6667          | 0.7333   |
| 0.0773        | 58.0  | 58   | 0.5142          | 0.8      |
| 0.0773        | 59.0  | 59   | 0.5593          | 0.8      |
| 0.035         | 60.0  | 60   | 0.7527          | 0.7      |
| 0.035         | 61.0  | 61   | 0.4706          | 0.8667   |
| 0.035         | 62.0  | 62   | 0.5345          | 0.8333   |
| 0.035         | 63.0  | 63   | 0.5804          | 0.7667   |
| 0.035         | 64.0  | 64   | 0.5549          | 0.7667   |
| 0.035         | 65.0  | 65   | 0.5665          | 0.8      |
| 0.035         | 66.0  | 66   | 0.3258          | 0.9333   |
| 0.035         | 67.0  | 67   | 0.4890          | 0.8333   |
| 0.035         | 68.0  | 68   | 0.4657          | 0.8333   |
| 0.035         | 69.0  | 69   | 0.6546          | 0.8      |
| 0.0192        | 70.0  | 70   | 0.4962          | 0.8667   |
| 0.0192        | 71.0  | 71   | 0.5801          | 0.8      |
| 0.0192        | 72.0  | 72   | 0.5365          | 0.8667   |
| 0.0192        | 73.0  | 73   | 0.3524          | 0.8667   |
| 0.0192        | 74.0  | 74   | 0.5291          | 0.8667   |
| 0.0192        | 75.0  | 75   | 0.4613          | 0.9333   |
| 0.0192        | 76.0  | 76   | 0.5031          | 0.8      |
| 0.0192        | 77.0  | 77   | 0.4986          | 0.8333   |
| 0.0192        | 78.0  | 78   | 0.6103          | 0.8      |
| 0.0192        | 79.0  | 79   | 0.5855          | 0.8333   |
| 0.0126        | 80.0  | 80   | 0.6136          | 0.7667   |
| 0.0126        | 81.0  | 81   | 0.5112          | 0.8667   |
| 0.0126        | 82.0  | 82   | 0.4770          | 0.8333   |
| 0.0126        | 83.0  | 83   | 0.4016          | 0.8667   |
| 0.0126        | 84.0  | 84   | 0.4946          | 0.8667   |
| 0.0126        | 85.0  | 85   | 0.5542          | 0.7667   |
| 0.0126        | 86.0  | 86   | 0.4037          | 0.8667   |
| 0.0126        | 87.0  | 87   | 0.4775          | 0.8      |
| 0.0126        | 88.0  | 88   | 0.5146          | 0.8333   |
| 0.0126        | 89.0  | 89   | 0.5603          | 0.7667   |
| 0.0072        | 90.0  | 90   | 0.5734          | 0.8      |
| 0.0072        | 91.0  | 91   | 0.5937          | 0.8      |
| 0.0072        | 92.0  | 92   | 0.5328          | 0.8      |
| 0.0072        | 93.0  | 93   | 0.4362          | 0.8667   |
| 0.0072        | 94.0  | 94   | 0.6317          | 0.7667   |
| 0.0072        | 95.0  | 95   | 0.4078          | 0.8667   |
| 0.0072        | 96.0  | 96   | 0.5680          | 0.8      |
| 0.0072        | 97.0  | 97   | 0.6209          | 0.8      |
| 0.0072        | 98.0  | 98   | 0.5360          | 0.8      |
| 0.0072        | 99.0  | 99   | 0.4784          | 0.8667   |
| 0.0093        | 100.0 | 100  | 0.4370          | 0.9      |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0

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