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# Emotion-Image-Classification-V3

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5048
- Accuracy: 0.6375

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 20   | 1.4141          | 0.5437   |
| No log        | 2.0   | 40   | 1.6711          | 0.4375   |
| No log        | 3.0   | 60   | 1.3988          | 0.6      |
| No log        | 4.0   | 80   | 1.5072          | 0.5625   |
| No log        | 5.0   | 100  | 1.3970          | 0.6125   |
| No log        | 6.0   | 120  | 1.3488          | 0.625    |
| No log        | 7.0   | 140  | 1.4599          | 0.5437   |
| No log        | 8.0   | 160  | 1.4678          | 0.5813   |
| No log        | 9.0   | 180  | 1.6072          | 0.5375   |
| No log        | 10.0  | 200  | 1.2243          | 0.6312   |
| No log        | 11.0  | 220  | 1.2860          | 0.5875   |
| No log        | 12.0  | 240  | 1.2472          | 0.5875   |
| No log        | 13.0  | 260  | 1.3423          | 0.5875   |
| No log        | 14.0  | 280  | 1.3879          | 0.5875   |
| No log        | 15.0  | 300  | 1.4201          | 0.575    |
| No log        | 16.0  | 320  | 1.5388          | 0.5312   |
| No log        | 17.0  | 340  | 1.5433          | 0.55     |
| No log        | 18.0  | 360  | 1.3812          | 0.5875   |
| No log        | 19.0  | 380  | 1.4629          | 0.5938   |
| No log        | 20.0  | 400  | 1.5240          | 0.525    |
| No log        | 21.0  | 420  | 1.4818          | 0.5437   |
| No log        | 22.0  | 440  | 1.4461          | 0.5687   |
| No log        | 23.0  | 460  | 1.3944          | 0.5875   |
| No log        | 24.0  | 480  | 1.6598          | 0.55     |
| 0.1882        | 25.0  | 500  | 1.4268          | 0.6188   |
| 0.1882        | 26.0  | 520  | 1.6246          | 0.5563   |
| 0.1882        | 27.0  | 540  | 1.3836          | 0.6125   |
| 0.1882        | 28.0  | 560  | 1.7652          | 0.4813   |
| 0.1882        | 29.0  | 580  | 1.4360          | 0.5625   |
| 0.1882        | 30.0  | 600  | 1.5103          | 0.55     |
| 0.1882        | 31.0  | 620  | 1.4546          | 0.5563   |
| 0.1882        | 32.0  | 640  | 1.4085          | 0.575    |
| 0.1882        | 33.0  | 660  | 1.4729          | 0.6062   |
| 0.1882        | 34.0  | 680  | 1.7415          | 0.5375   |
| 0.1882        | 35.0  | 700  | 1.7349          | 0.5375   |
| 0.1882        | 36.0  | 720  | 1.6331          | 0.5687   |
| 0.1882        | 37.0  | 740  | 1.5159          | 0.6062   |
| 0.1882        | 38.0  | 760  | 1.5464          | 0.5875   |
| 0.1882        | 39.0  | 780  | 1.5402          | 0.5938   |
| 0.1882        | 40.0  | 800  | 1.5403          | 0.6      |
| 0.1882        | 41.0  | 820  | 1.4509          | 0.65     |
| 0.1882        | 42.0  | 840  | 1.7641          | 0.5437   |
| 0.1882        | 43.0  | 860  | 1.5503          | 0.5813   |
| 0.1882        | 44.0  | 880  | 1.6178          | 0.5687   |
| 0.1882        | 45.0  | 900  | 1.5877          | 0.6062   |
| 0.1882        | 46.0  | 920  | 1.7210          | 0.55     |
| 0.1882        | 47.0  | 940  | 1.5960          | 0.6188   |
| 0.1882        | 48.0  | 960  | 1.7922          | 0.55     |
| 0.1882        | 49.0  | 980  | 2.0035          | 0.525    |
| 0.1299        | 50.0  | 1000 | 1.8269          | 0.5062   |
| 0.1299        | 51.0  | 1020 | 1.6933          | 0.5687   |
| 0.1299        | 52.0  | 1040 | 1.7252          | 0.5312   |
| 0.1299        | 53.0  | 1060 | 1.6312          | 0.6      |
| 0.1299        | 54.0  | 1080 | 1.8208          | 0.5375   |
| 0.1299        | 55.0  | 1100 | 1.7589          | 0.575    |
| 0.1299        | 56.0  | 1120 | 1.7185          | 0.5875   |
| 0.1299        | 57.0  | 1140 | 1.7227          | 0.5437   |
| 0.1299        | 58.0  | 1160 | 1.8849          | 0.5188   |
| 0.1299        | 59.0  | 1180 | 1.7565          | 0.5687   |
| 0.1299        | 60.0  | 1200 | 1.6048          | 0.6062   |
| 0.1299        | 61.0  | 1220 | 1.5088          | 0.6125   |
| 0.1299        | 62.0  | 1240 | 1.6270          | 0.5687   |
| 0.1299        | 63.0  | 1260 | 1.5913          | 0.625    |
| 0.1299        | 64.0  | 1280 | 1.7789          | 0.5625   |
| 0.1299        | 65.0  | 1300 | 1.7923          | 0.55     |
| 0.1299        | 66.0  | 1320 | 1.9365          | 0.575    |
| 0.1299        | 67.0  | 1340 | 1.7365          | 0.5938   |
| 0.1299        | 68.0  | 1360 | 1.8584          | 0.55     |
| 0.1299        | 69.0  | 1380 | 1.9811          | 0.5062   |
| 0.1299        | 70.0  | 1400 | 1.9433          | 0.55     |
| 0.1299        | 71.0  | 1420 | 1.7644          | 0.575    |
| 0.1299        | 72.0  | 1440 | 1.7661          | 0.6      |
| 0.1299        | 73.0  | 1460 | 1.8884          | 0.5687   |
| 0.1299        | 74.0  | 1480 | 1.7504          | 0.5813   |
| 0.0774        | 75.0  | 1500 | 1.9648          | 0.5687   |
| 0.0774        | 76.0  | 1520 | 1.8968          | 0.5437   |
| 0.0774        | 77.0  | 1540 | 1.7752          | 0.5875   |
| 0.0774        | 78.0  | 1560 | 1.7504          | 0.625    |
| 0.0774        | 79.0  | 1580 | 1.7458          | 0.6      |
| 0.0774        | 80.0  | 1600 | 1.8044          | 0.5938   |
| 0.0774        | 81.0  | 1620 | 1.6748          | 0.5813   |
| 0.0774        | 82.0  | 1640 | 1.7661          | 0.575    |
| 0.0774        | 83.0  | 1660 | 1.8534          | 0.575    |
| 0.0774        | 84.0  | 1680 | 1.7733          | 0.6125   |
| 0.0774        | 85.0  | 1700 | 1.7857          | 0.575    |
| 0.0774        | 86.0  | 1720 | 1.7397          | 0.6      |
| 0.0774        | 87.0  | 1740 | 1.7496          | 0.5813   |
| 0.0774        | 88.0  | 1760 | 1.8774          | 0.5813   |
| 0.0774        | 89.0  | 1780 | 1.6830          | 0.5938   |
| 0.0774        | 90.0  | 1800 | 1.9231          | 0.5563   |
| 0.0774        | 91.0  | 1820 | 1.8051          | 0.5875   |
| 0.0774        | 92.0  | 1840 | 1.8424          | 0.5938   |
| 0.0774        | 93.0  | 1860 | 1.8644          | 0.575    |
| 0.0774        | 94.0  | 1880 | 1.8415          | 0.5687   |
| 0.0774        | 95.0  | 1900 | 1.8917          | 0.55     |
| 0.0774        | 96.0  | 1920 | 1.8964          | 0.5625   |
| 0.0774        | 97.0  | 1940 | 1.6416          | 0.5875   |
| 0.0774        | 98.0  | 1960 | 1.7067          | 0.625    |
| 0.0774        | 99.0  | 1980 | 1.7533          | 0.5938   |
| 0.0569        | 100.0 | 2000 | 1.8181          | 0.5563   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.15.0
- Tokenizers 0.15.1

anger contempt disgust fear happy neutral sad surprise