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# Fer_vit_jaffe_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: 0.4000
- Accuracy: 0.8667

## 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    | 2.2045          | 0.1      |
| No log        | 2.0   | 2    | 2.0846          | 0.1333   |
| No log        | 3.0   | 3    | 2.1060          | 0.1      |
| No log        | 4.0   | 4    | 1.9913          | 0.1333   |
| No log        | 5.0   | 5    | 1.9980          | 0.1      |
| No log        | 6.0   | 6    | 1.8330          | 0.3      |
| No log        | 7.0   | 7    | 1.9518          | 0.1333   |
| No log        | 8.0   | 8    | 1.9296          | 0.1667   |
| No log        | 9.0   | 9    | 1.8688          | 0.3333   |
| 1.9932        | 10.0  | 10   | 1.7509          | 0.3667   |
| 1.9932        | 11.0  | 11   | 1.6357          | 0.4      |
| 1.9932        | 12.0  | 12   | 1.5627          | 0.3667   |
| 1.9932        | 13.0  | 13   | 1.6459          | 0.3      |
| 1.9932        | 14.0  | 14   | 1.5215          | 0.4      |
| 1.9932        | 15.0  | 15   | 1.5421          | 0.3667   |
| 1.9932        | 16.0  | 16   | 1.4164          | 0.5      |
| 1.9932        | 17.0  | 17   | 1.4463          | 0.3667   |
| 1.9932        | 18.0  | 18   | 1.2905          | 0.4667   |
| 1.9932        | 19.0  | 19   | 1.2456          | 0.6      |
| 1.2161        | 20.0  | 20   | 1.2170          | 0.5667   |
| 1.2161        | 21.0  | 21   | 1.0307          | 0.6      |
| 1.2161        | 22.0  | 22   | 1.1198          | 0.6      |
| 1.2161        | 23.0  | 23   | 1.1648          | 0.5      |
| 1.2161        | 24.0  | 24   | 1.0260          | 0.6      |
| 1.2161        | 25.0  | 25   | 1.3020          | 0.5      |
| 1.2161        | 26.0  | 26   | 0.9796          | 0.6333   |
| 1.2161        | 27.0  | 27   | 0.9824          | 0.6667   |
| 1.2161        | 28.0  | 28   | 0.8884          | 0.7      |
| 1.2161        | 29.0  | 29   | 0.9246          | 0.6333   |
| 0.5116        | 30.0  | 30   | 0.8455          | 0.7333   |
| 0.5116        | 31.0  | 31   | 0.7960          | 0.7      |
| 0.5116        | 32.0  | 32   | 0.8179          | 0.7333   |
| 0.5116        | 33.0  | 33   | 0.8721          | 0.6667   |
| 0.5116        | 34.0  | 34   | 0.8279          | 0.7667   |
| 0.5116        | 35.0  | 35   | 0.6486          | 0.7667   |
| 0.5116        | 36.0  | 36   | 0.6816          | 0.7333   |
| 0.5116        | 37.0  | 37   | 0.8016          | 0.7333   |
| 0.5116        | 38.0  | 38   | 0.6464          | 0.8      |
| 0.5116        | 39.0  | 39   | 0.6922          | 0.7667   |
| 0.2101        | 40.0  | 40   | 0.6768          | 0.7667   |
| 0.2101        | 41.0  | 41   | 0.6408          | 0.7667   |
| 0.2101        | 42.0  | 42   | 0.5335          | 0.8333   |
| 0.2101        | 43.0  | 43   | 0.4862          | 0.8333   |
| 0.2101        | 44.0  | 44   | 0.3713          | 0.8667   |
| 0.2101        | 45.0  | 45   | 0.4382          | 0.8333   |
| 0.2101        | 46.0  | 46   | 0.6664          | 0.7667   |
| 0.2101        | 47.0  | 47   | 0.4865          | 0.8333   |
| 0.2101        | 48.0  | 48   | 0.4411          | 0.8      |
| 0.2101        | 49.0  | 49   | 0.4707          | 0.8667   |
| 0.0921        | 50.0  | 50   | 0.6355          | 0.7667   |
| 0.0921        | 51.0  | 51   | 0.3975          | 0.9      |
| 0.0921        | 52.0  | 52   | 0.4261          | 0.8333   |
| 0.0921        | 53.0  | 53   | 0.3944          | 0.8      |
| 0.0921        | 54.0  | 54   | 0.2987          | 0.9333   |
| 0.0921        | 55.0  | 55   | 0.4845          | 0.8667   |
| 0.0921        | 56.0  | 56   | 0.5880          | 0.7667   |
| 0.0921        | 57.0  | 57   | 0.6478          | 0.8333   |
| 0.0921        | 58.0  | 58   | 0.4498          | 0.8      |
| 0.0921        | 59.0  | 59   | 0.3165          | 0.8667   |
| 0.0488        | 60.0  | 60   | 0.5294          | 0.8333   |
| 0.0488        | 61.0  | 61   | 0.6030          | 0.8333   |
| 0.0488        | 62.0  | 62   | 0.4018          | 0.8333   |
| 0.0488        | 63.0  | 63   | 0.5076          | 0.8333   |
| 0.0488        | 64.0  | 64   | 0.5128          | 0.8667   |
| 0.0488        | 65.0  | 65   | 0.5164          | 0.8667   |
| 0.0488        | 66.0  | 66   | 0.4238          | 0.8333   |
| 0.0488        | 67.0  | 67   | 0.5057          | 0.8333   |
| 0.0488        | 68.0  | 68   | 0.6507          | 0.7667   |
| 0.0488        | 69.0  | 69   | 0.4623          | 0.8667   |
| 0.0336        | 70.0  | 70   | 0.4230          | 0.8333   |
| 0.0336        | 71.0  | 71   | 0.4669          | 0.8333   |
| 0.0336        | 72.0  | 72   | 0.4836          | 0.8333   |
| 0.0336        | 73.0  | 73   | 0.3458          | 0.9333   |
| 0.0336        | 74.0  | 74   | 0.4629          | 0.8667   |
| 0.0336        | 75.0  | 75   | 0.4426          | 0.7667   |
| 0.0336        | 76.0  | 76   | 0.4735          | 0.8      |
| 0.0336        | 77.0  | 77   | 0.5138          | 0.7667   |
| 0.0336        | 78.0  | 78   | 0.4728          | 0.8333   |
| 0.0336        | 79.0  | 79   | 0.3224          | 0.8667   |
| 0.0204        | 80.0  | 80   | 0.2733          | 0.8667   |
| 0.0204        | 81.0  | 81   | 0.4948          | 0.8333   |
| 0.0204        | 82.0  | 82   | 0.3923          | 0.9      |
| 0.0204        | 83.0  | 83   | 0.2380          | 0.9      |
| 0.0204        | 84.0  | 84   | 0.4343          | 0.8667   |
| 0.0204        | 85.0  | 85   | 0.4008          | 0.8      |
| 0.0204        | 86.0  | 86   | 0.3960          | 0.9      |
| 0.0204        | 87.0  | 87   | 0.4185          | 0.8667   |
| 0.0204        | 88.0  | 88   | 0.4394          | 0.8      |
| 0.0204        | 89.0  | 89   | 0.3055          | 0.9      |
| 0.0113        | 90.0  | 90   | 0.4782          | 0.7333   |
| 0.0113        | 91.0  | 91   | 0.4763          | 0.8667   |
| 0.0113        | 92.0  | 92   | 0.4404          | 0.9      |
| 0.0113        | 93.0  | 93   | 0.2787          | 0.9      |
| 0.0113        | 94.0  | 94   | 0.3599          | 0.9      |
| 0.0113        | 95.0  | 95   | 0.5665          | 0.8333   |
| 0.0113        | 96.0  | 96   | 0.3193          | 0.9333   |
| 0.0113        | 97.0  | 97   | 0.3259          | 0.8667   |
| 0.0113        | 98.0  | 98   | 0.3528          | 0.9333   |
| 0.0113        | 99.0  | 99   | 0.3905          | 0.8667   |
| 0.009         | 100.0 | 100  | 0.4000          | 0.8667   |


### Framework versions

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

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