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# jaffe_V2_100_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: 0.6446
- Accuracy: 0.7333

## 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: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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    | 2.2977          | 0.1333   |
| No log        | 2.0   | 2    | 2.0741          | 0.1      |
| No log        | 3.0   | 3    | 2.0275          | 0.2333   |
| No log        | 4.0   | 4    | 2.1234          | 0.0667   |
| No log        | 5.0   | 5    | 2.0852          | 0.0333   |
| No log        | 6.0   | 6    | 2.0259          | 0.1667   |
| No log        | 7.0   | 7    | 2.0362          | 0.2667   |
| No log        | 8.0   | 8    | 2.0153          | 0.2333   |
| No log        | 9.0   | 9    | 1.7472          | 0.3333   |
| 1.9971        | 10.0  | 10   | 1.9598          | 0.2      |
| 1.9971        | 11.0  | 11   | 1.9367          | 0.3333   |
| 1.9971        | 12.0  | 12   | 1.8312          | 0.3333   |
| 1.9971        | 13.0  | 13   | 1.7299          | 0.3      |
| 1.9971        | 14.0  | 14   | 1.6306          | 0.4333   |
| 1.9971        | 15.0  | 15   | 1.5377          | 0.4333   |
| 1.9971        | 16.0  | 16   | 1.4326          | 0.5333   |
| 1.9971        | 17.0  | 17   | 1.5047          | 0.4      |
| 1.9971        | 18.0  | 18   | 1.4929          | 0.4333   |
| 1.9971        | 19.0  | 19   | 1.5326          | 0.4      |
| 1.5087        | 20.0  | 20   | 1.5017          | 0.4667   |
| 1.5087        | 21.0  | 21   | 1.4978          | 0.4333   |
| 1.5087        | 22.0  | 22   | 1.2678          | 0.5667   |
| 1.5087        | 23.0  | 23   | 1.2538          | 0.5      |
| 1.5087        | 24.0  | 24   | 1.2526          | 0.5      |
| 1.5087        | 25.0  | 25   | 1.3660          | 0.4      |
| 1.5087        | 26.0  | 26   | 1.3206          | 0.5      |
| 1.5087        | 27.0  | 27   | 1.2053          | 0.4333   |
| 1.5087        | 28.0  | 28   | 1.1457          | 0.6333   |
| 1.5087        | 29.0  | 29   | 1.0761          | 0.6      |
| 1.007         | 30.0  | 30   | 1.1556          | 0.5      |
| 1.007         | 31.0  | 31   | 1.0172          | 0.6333   |
| 1.007         | 32.0  | 32   | 1.1851          | 0.5333   |
| 1.007         | 33.0  | 33   | 1.0535          | 0.5333   |
| 1.007         | 34.0  | 34   | 1.1161          | 0.5333   |
| 1.007         | 35.0  | 35   | 0.9928          | 0.5667   |
| 1.007         | 36.0  | 36   | 0.9970          | 0.7      |
| 1.007         | 37.0  | 37   | 1.1090          | 0.4667   |
| 1.007         | 38.0  | 38   | 0.9536          | 0.6      |
| 1.007         | 39.0  | 39   | 1.2752          | 0.5      |
| 0.6664        | 40.0  | 40   | 0.8948          | 0.6667   |
| 0.6664        | 41.0  | 41   | 0.8891          | 0.6667   |
| 0.6664        | 42.0  | 42   | 0.8382          | 0.6333   |
| 0.6664        | 43.0  | 43   | 0.7498          | 0.7      |
| 0.6664        | 44.0  | 44   | 0.8668          | 0.6667   |
| 0.6664        | 45.0  | 45   | 1.1427          | 0.6667   |
| 0.6664        | 46.0  | 46   | 0.8066          | 0.6      |
| 0.6664        | 47.0  | 47   | 0.9161          | 0.6333   |
| 0.6664        | 48.0  | 48   | 0.8266          | 0.6      |
| 0.6664        | 49.0  | 49   | 0.9943          | 0.5667   |
| 0.469         | 50.0  | 50   | 0.6892          | 0.6333   |
| 0.469         | 51.0  | 51   | 0.7529          | 0.7667   |
| 0.469         | 52.0  | 52   | 0.9834          | 0.5333   |
| 0.469         | 53.0  | 53   | 0.8994          | 0.6      |
| 0.469         | 54.0  | 54   | 0.6394          | 0.7667   |
| 0.469         | 55.0  | 55   | 0.6854          | 0.7      |
| 0.469         | 56.0  | 56   | 0.6051          | 0.8      |
| 0.469         | 57.0  | 57   | 0.8493          | 0.6667   |
| 0.469         | 58.0  | 58   | 0.6897          | 0.7333   |
| 0.469         | 59.0  | 59   | 0.6698          | 0.6667   |
| 0.3604        | 60.0  | 60   | 0.6562          | 0.7667   |
| 0.3604        | 61.0  | 61   | 0.7638          | 0.6      |
| 0.3604        | 62.0  | 62   | 0.6217          | 0.7333   |
| 0.3604        | 63.0  | 63   | 0.7635          | 0.7      |
| 0.3604        | 64.0  | 64   | 0.7777          | 0.7667   |
| 0.3604        | 65.0  | 65   | 0.6505          | 0.8      |
| 0.3604        | 66.0  | 66   | 0.6469          | 0.7333   |
| 0.3604        | 67.0  | 67   | 0.7266          | 0.7333   |
| 0.3604        | 68.0  | 68   | 0.7613          | 0.6667   |
| 0.3604        | 69.0  | 69   | 0.4647          | 0.8      |
| 0.2726        | 70.0  | 70   | 0.6390          | 0.7      |
| 0.2726        | 71.0  | 71   | 0.6155          | 0.7333   |
| 0.2726        | 72.0  | 72   | 0.6113          | 0.8      |
| 0.2726        | 73.0  | 73   | 0.5648          | 0.8      |
| 0.2726        | 74.0  | 74   | 0.7042          | 0.7      |
| 0.2726        | 75.0  | 75   | 0.6263          | 0.8333   |
| 0.2726        | 76.0  | 76   | 0.7464          | 0.7333   |
| 0.2726        | 77.0  | 77   | 0.7640          | 0.6333   |
| 0.2726        | 78.0  | 78   | 0.7129          | 0.7667   |
| 0.2726        | 79.0  | 79   | 0.7362          | 0.7333   |
| 0.2157        | 80.0  | 80   | 0.7122          | 0.7667   |
| 0.2157        | 81.0  | 81   | 0.5565          | 0.7333   |
| 0.2157        | 82.0  | 82   | 0.6734          | 0.7667   |
| 0.2157        | 83.0  | 83   | 0.6057          | 0.7      |
| 0.2157        | 84.0  | 84   | 0.5287          | 0.7667   |
| 0.2157        | 85.0  | 85   | 0.7490          | 0.7333   |
| 0.2157        | 86.0  | 86   | 0.5841          | 0.7333   |
| 0.2157        | 87.0  | 87   | 0.5641          | 0.7667   |
| 0.2157        | 88.0  | 88   | 0.8243          | 0.6667   |
| 0.2157        | 89.0  | 89   | 0.5287          | 0.7667   |
| 0.1946        | 90.0  | 90   | 1.0455          | 0.7      |
| 0.1946        | 91.0  | 91   | 0.6091          | 0.7333   |
| 0.1946        | 92.0  | 92   | 0.5152          | 0.7667   |
| 0.1946        | 93.0  | 93   | 0.5850          | 0.8      |
| 0.1946        | 94.0  | 94   | 0.5806          | 0.7333   |
| 0.1946        | 95.0  | 95   | 0.6017          | 0.7667   |
| 0.1946        | 96.0  | 96   | 0.5606          | 0.7667   |
| 0.1946        | 97.0  | 97   | 0.5931          | 0.7667   |
| 0.1946        | 98.0  | 98   | 0.5299          | 0.7667   |
| 0.1946        | 99.0  | 99   | 0.7117          | 0.7333   |
| 0.1647        | 100.0 | 100  | 0.6446          | 0.7333   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0

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