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# Fer_vit_jaffe_crop_GOOGLE_1

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: 0.2564
- 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: 1
- 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    | 2.8373          | 0.1667   |
| No log        | 2.0   | 2    | 2.6726          | 0.1667   |
| No log        | 3.0   | 3    | 2.4131          | 0.1      |
| No log        | 4.0   | 4    | 2.1618          | 0.1      |
| No log        | 5.0   | 5    | 1.9925          | 0.2333   |
| No log        | 6.0   | 6    | 2.0082          | 0.1      |
| No log        | 7.0   | 7    | 2.0631          | 0.1667   |
| No log        | 8.0   | 8    | 1.9582          | 0.1667   |
| No log        | 9.0   | 9    | 1.9078          | 0.1      |
| 2.2546        | 10.0  | 10   | 1.8412          | 0.2333   |
| 2.2546        | 11.0  | 11   | 1.7763          | 0.3      |
| 2.2546        | 12.0  | 12   | 1.7447          | 0.4      |
| 2.2546        | 13.0  | 13   | 1.6744          | 0.2667   |
| 2.2546        | 14.0  | 14   | 1.6643          | 0.3333   |
| 2.2546        | 15.0  | 15   | 1.5688          | 0.4333   |
| 2.2546        | 16.0  | 16   | 1.5458          | 0.4333   |
| 2.2546        | 17.0  | 17   | 1.5068          | 0.5      |
| 2.2546        | 18.0  | 18   | 1.4505          | 0.4333   |
| 2.2546        | 19.0  | 19   | 1.3640          | 0.4      |
| 1.352         | 20.0  | 20   | 1.3531          | 0.3667   |
| 1.352         | 21.0  | 21   | 1.3028          | 0.4667   |
| 1.352         | 22.0  | 22   | 1.2587          | 0.5      |
| 1.352         | 23.0  | 23   | 1.3161          | 0.5      |
| 1.352         | 24.0  | 24   | 1.2402          | 0.4333   |
| 1.352         | 25.0  | 25   | 1.1801          | 0.4333   |
| 1.352         | 26.0  | 26   | 1.1508          | 0.5333   |
| 1.352         | 27.0  | 27   | 1.0463          | 0.6667   |
| 1.352         | 28.0  | 28   | 1.0176          | 0.5      |
| 1.352         | 29.0  | 29   | 1.0326          | 0.5333   |
| 0.6369        | 30.0  | 30   | 0.9021          | 0.6      |
| 0.6369        | 31.0  | 31   | 0.9485          | 0.5      |
| 0.6369        | 32.0  | 32   | 0.8393          | 0.6      |
| 0.6369        | 33.0  | 33   | 0.9536          | 0.5667   |
| 0.6369        | 34.0  | 34   | 0.8815          | 0.6333   |
| 0.6369        | 35.0  | 35   | 0.8329          | 0.6333   |
| 0.6369        | 36.0  | 36   | 0.7946          | 0.7333   |
| 0.6369        | 37.0  | 37   | 0.8582          | 0.6333   |
| 0.6369        | 38.0  | 38   | 0.7418          | 0.7667   |
| 0.6369        | 39.0  | 39   | 0.7232          | 0.7667   |
| 0.2532        | 40.0  | 40   | 0.7750          | 0.7333   |
| 0.2532        | 41.0  | 41   | 0.7209          | 0.7333   |
| 0.2532        | 42.0  | 42   | 0.6851          | 0.7      |
| 0.2532        | 43.0  | 43   | 0.6823          | 0.7667   |
| 0.2532        | 44.0  | 44   | 0.5122          | 0.7667   |
| 0.2532        | 45.0  | 45   | 0.5930          | 0.7667   |
| 0.2532        | 46.0  | 46   | 0.6531          | 0.7333   |
| 0.2532        | 47.0  | 47   | 0.5651          | 0.8      |
| 0.2532        | 48.0  | 48   | 0.5014          | 0.8667   |
| 0.2532        | 49.0  | 49   | 0.4853          | 0.8333   |
| 0.098         | 50.0  | 50   | 0.4904          | 0.8667   |
| 0.098         | 51.0  | 51   | 0.6781          | 0.7      |
| 0.098         | 52.0  | 52   | 0.6540          | 0.8      |
| 0.098         | 53.0  | 53   | 0.7150          | 0.7      |
| 0.098         | 54.0  | 54   | 0.5828          | 0.8      |
| 0.098         | 55.0  | 55   | 0.5115          | 0.8      |
| 0.098         | 56.0  | 56   | 0.4744          | 0.8      |
| 0.098         | 57.0  | 57   | 0.4548          | 0.8667   |
| 0.098         | 58.0  | 58   | 0.4936          | 0.8667   |
| 0.098         | 59.0  | 59   | 0.3534          | 0.8667   |
| 0.0473        | 60.0  | 60   | 0.6354          | 0.7333   |
| 0.0473        | 61.0  | 61   | 0.4243          | 0.9      |
| 0.0473        | 62.0  | 62   | 0.2744          | 0.9333   |
| 0.0473        | 63.0  | 63   | 0.4937          | 0.8333   |
| 0.0473        | 64.0  | 64   | 0.3869          | 0.9      |
| 0.0473        | 65.0  | 65   | 0.5379          | 0.8667   |
| 0.0473        | 66.0  | 66   | 0.4878          | 0.8      |
| 0.0473        | 67.0  | 67   | 0.6310          | 0.7667   |
| 0.0473        | 68.0  | 68   | 0.5021          | 0.8      |
| 0.0473        | 69.0  | 69   | 0.5109          | 0.8667   |
| 0.0218        | 70.0  | 70   | 0.4052          | 0.8667   |
| 0.0218        | 71.0  | 71   | 0.3340          | 0.9      |
| 0.0218        | 72.0  | 72   | 0.4823          | 0.8333   |
| 0.0218        | 73.0  | 73   | 0.2980          | 0.9      |
| 0.0218        | 74.0  | 74   | 0.3515          | 0.8667   |
| 0.0218        | 75.0  | 75   | 0.4199          | 0.8      |
| 0.0218        | 76.0  | 76   | 0.4145          | 0.9      |
| 0.0218        | 77.0  | 77   | 0.4639          | 0.7667   |
| 0.0218        | 78.0  | 78   | 0.3376          | 0.8667   |
| 0.0218        | 79.0  | 79   | 0.3546          | 0.8667   |
| 0.0121        | 80.0  | 80   | 0.3863          | 0.8667   |
| 0.0121        | 81.0  | 81   | 0.3637          | 0.8667   |
| 0.0121        | 82.0  | 82   | 0.3622          | 0.8667   |
| 0.0121        | 83.0  | 83   | 0.4142          | 0.8667   |
| 0.0121        | 84.0  | 84   | 0.4829          | 0.7667   |
| 0.0121        | 85.0  | 85   | 0.4039          | 0.8667   |
| 0.0121        | 86.0  | 86   | 0.3893          | 0.9      |
| 0.0121        | 87.0  | 87   | 0.5483          | 0.8333   |
| 0.0121        | 88.0  | 88   | 0.3928          | 0.8333   |
| 0.0121        | 89.0  | 89   | 0.3336          | 0.8667   |
| 0.0077        | 90.0  | 90   | 0.2689          | 0.9333   |
| 0.0077        | 91.0  | 91   | 0.3586          | 0.9333   |
| 0.0077        | 92.0  | 92   | 0.4284          | 0.9      |
| 0.0077        | 93.0  | 93   | 0.4150          | 0.8333   |
| 0.0077        | 94.0  | 94   | 0.2941          | 0.9      |
| 0.0077        | 95.0  | 95   | 0.2634          | 0.8667   |
| 0.0077        | 96.0  | 96   | 0.2631          | 0.9333   |
| 0.0077        | 97.0  | 97   | 0.3490          | 0.9333   |
| 0.0077        | 98.0  | 98   | 0.3602          | 0.9      |
| 0.0077        | 99.0  | 99   | 0.2326          | 0.9333   |
| 0.0065        | 100.0 | 100  | 0.2564          | 0.9      |


### Framework versions

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

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