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# Fer_vit_jaffe_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: 0.4137
- Accuracy: 0.8333

## 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    | 2.4981          | 0.1333   |
| No log        | 2.0   | 2    | 2.4022          | 0.1333   |
| No log        | 3.0   | 3    | 2.2167          | 0.1      |
| No log        | 4.0   | 4    | 2.0743          | 0.1333   |
| No log        | 5.0   | 5    | 1.9393          | 0.1      |
| No log        | 6.0   | 6    | 2.0201          | 0.1667   |
| No log        | 7.0   | 7    | 1.9793          | 0.1667   |
| No log        | 8.0   | 8    | 1.9287          | 0.2      |
| No log        | 9.0   | 9    | 1.8316          | 0.1667   |
| 2.1031        | 10.0  | 10   | 1.6923          | 0.4667   |
| 2.1031        | 11.0  | 11   | 1.7380          | 0.2667   |
| 2.1031        | 12.0  | 12   | 1.7164          | 0.3333   |
| 2.1031        | 13.0  | 13   | 1.6525          | 0.3333   |
| 2.1031        | 14.0  | 14   | 1.5759          | 0.3333   |
| 2.1031        | 15.0  | 15   | 1.5251          | 0.3333   |
| 2.1031        | 16.0  | 16   | 1.4557          | 0.4333   |
| 2.1031        | 17.0  | 17   | 1.3619          | 0.4333   |
| 2.1031        | 18.0  | 18   | 1.2880          | 0.4333   |
| 2.1031        | 19.0  | 19   | 1.2356          | 0.5667   |
| 1.2981        | 20.0  | 20   | 1.1369          | 0.6      |
| 1.2981        | 21.0  | 21   | 1.1489          | 0.5      |
| 1.2981        | 22.0  | 22   | 1.0756          | 0.7      |
| 1.2981        | 23.0  | 23   | 1.0136          | 0.5333   |
| 1.2981        | 24.0  | 24   | 1.0509          | 0.5333   |
| 1.2981        | 25.0  | 25   | 0.9975          | 0.6      |
| 1.2981        | 26.0  | 26   | 0.9895          | 0.6      |
| 1.2981        | 27.0  | 27   | 0.9735          | 0.6      |
| 1.2981        | 28.0  | 28   | 0.9328          | 0.6      |
| 1.2981        | 29.0  | 29   | 0.9559          | 0.6667   |
| 0.5735        | 30.0  | 30   | 0.8359          | 0.7667   |
| 0.5735        | 31.0  | 31   | 0.8023          | 0.7667   |
| 0.5735        | 32.0  | 32   | 0.8285          | 0.6333   |
| 0.5735        | 33.0  | 33   | 0.7287          | 0.7      |
| 0.5735        | 34.0  | 34   | 0.7043          | 0.7667   |
| 0.5735        | 35.0  | 35   | 0.8992          | 0.7333   |
| 0.5735        | 36.0  | 36   | 0.8664          | 0.7667   |
| 0.5735        | 37.0  | 37   | 0.8023          | 0.7333   |
| 0.5735        | 38.0  | 38   | 0.6910          | 0.7667   |
| 0.5735        | 39.0  | 39   | 0.8197          | 0.6667   |
| 0.2477        | 40.0  | 40   | 0.5915          | 0.7667   |
| 0.2477        | 41.0  | 41   | 0.9184          | 0.6333   |
| 0.2477        | 42.0  | 42   | 0.6734          | 0.7      |
| 0.2477        | 43.0  | 43   | 0.9225          | 0.7      |
| 0.2477        | 44.0  | 44   | 0.5961          | 0.8      |
| 0.2477        | 45.0  | 45   | 0.7012          | 0.7      |
| 0.2477        | 46.0  | 46   | 0.9223          | 0.6      |
| 0.2477        | 47.0  | 47   | 0.5819          | 0.7      |
| 0.2477        | 48.0  | 48   | 0.7171          | 0.7333   |
| 0.2477        | 49.0  | 49   | 0.6416          | 0.7667   |
| 0.1117        | 50.0  | 50   | 0.8718          | 0.7      |
| 0.1117        | 51.0  | 51   | 0.4941          | 0.8      |
| 0.1117        | 52.0  | 52   | 0.7385          | 0.8      |
| 0.1117        | 53.0  | 53   | 0.6660          | 0.8333   |
| 0.1117        | 54.0  | 54   | 0.6988          | 0.8667   |
| 0.1117        | 55.0  | 55   | 0.7074          | 0.7667   |
| 0.1117        | 56.0  | 56   | 0.5847          | 0.8      |
| 0.1117        | 57.0  | 57   | 0.6636          | 0.8      |
| 0.1117        | 58.0  | 58   | 0.5520          | 0.8333   |
| 0.1117        | 59.0  | 59   | 0.6299          | 0.7667   |
| 0.0591        | 60.0  | 60   | 0.6717          | 0.7667   |
| 0.0591        | 61.0  | 61   | 0.4874          | 0.8333   |
| 0.0591        | 62.0  | 62   | 0.4603          | 0.8      |
| 0.0591        | 63.0  | 63   | 0.5516          | 0.7333   |
| 0.0591        | 64.0  | 64   | 0.4729          | 0.8      |
| 0.0591        | 65.0  | 65   | 0.5710          | 0.7667   |
| 0.0591        | 66.0  | 66   | 0.8985          | 0.7      |
| 0.0591        | 67.0  | 67   | 0.8074          | 0.7667   |
| 0.0591        | 68.0  | 68   | 0.5652          | 0.8      |
| 0.0591        | 69.0  | 69   | 0.5538          | 0.8333   |
| 0.0296        | 70.0  | 70   | 0.5727          | 0.7333   |
| 0.0296        | 71.0  | 71   | 0.6359          | 0.8      |
| 0.0296        | 72.0  | 72   | 0.6932          | 0.7333   |
| 0.0296        | 73.0  | 73   | 0.9025          | 0.6667   |
| 0.0296        | 74.0  | 74   | 0.6639          | 0.7333   |
| 0.0296        | 75.0  | 75   | 0.8385          | 0.7333   |
| 0.0296        | 76.0  | 76   | 0.5827          | 0.8      |
| 0.0296        | 77.0  | 77   | 0.5443          | 0.8667   |
| 0.0296        | 78.0  | 78   | 0.6330          | 0.8333   |
| 0.0296        | 79.0  | 79   | 0.6706          | 0.7333   |
| 0.0175        | 80.0  | 80   | 0.7803          | 0.8      |
| 0.0175        | 81.0  | 81   | 0.5401          | 0.8      |
| 0.0175        | 82.0  | 82   | 0.6806          | 0.8333   |
| 0.0175        | 83.0  | 83   | 0.3827          | 0.8      |
| 0.0175        | 84.0  | 84   | 0.7853          | 0.8      |
| 0.0175        | 85.0  | 85   | 0.4391          | 0.8333   |
| 0.0175        | 86.0  | 86   | 0.6061          | 0.8      |
| 0.0175        | 87.0  | 87   | 0.4797          | 0.8      |
| 0.0175        | 88.0  | 88   | 0.4386          | 0.8333   |
| 0.0175        | 89.0  | 89   | 0.6556          | 0.8      |
| 0.0121        | 90.0  | 90   | 0.7927          | 0.8      |
| 0.0121        | 91.0  | 91   | 0.4925          | 0.8333   |
| 0.0121        | 92.0  | 92   | 0.6280          | 0.7667   |
| 0.0121        | 93.0  | 93   | 0.3561          | 0.9      |
| 0.0121        | 94.0  | 94   | 0.6058          | 0.8333   |
| 0.0121        | 95.0  | 95   | 0.5086          | 0.8      |
| 0.0121        | 96.0  | 96   | 0.3854          | 0.8667   |
| 0.0121        | 97.0  | 97   | 0.8370          | 0.7667   |
| 0.0121        | 98.0  | 98   | 0.6506          | 0.7333   |
| 0.0121        | 99.0  | 99   | 0.6100          | 0.7667   |
| 0.0088        | 100.0 | 100  | 0.4137          | 0.8333   |


### Framework versions

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

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