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# finetuned-AffectNet

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8122
- Accuracy: 0.7345

## 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-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.0686        | 1.0   | 163   | 2.0963          | 0.1549   |
| 1.7148        | 2.0   | 327   | 1.7250          | 0.2943   |
| 1.4591        | 3.0   | 490   | 1.4418          | 0.4204   |
| 1.3351        | 4.0   | 654   | 1.2648          | 0.5194   |
| 1.1343        | 5.0   | 817   | 1.0728          | 0.5908   |
| 1.1022        | 6.0   | 981   | 0.9741          | 0.6355   |
| 1.0476        | 7.0   | 1144  | 0.9203          | 0.6631   |
| 1.0049        | 8.0   | 1308  | 0.8769          | 0.6760   |
| 0.9561        | 9.0   | 1471  | 0.8438          | 0.6966   |
| 0.9409        | 10.0  | 1635  | 0.8283          | 0.6988   |
| 0.9419        | 11.0  | 1798  | 0.7867          | 0.7164   |
| 0.89          | 12.0  | 1962  | 0.7858          | 0.7139   |
| 0.8761        | 13.0  | 2125  | 0.7704          | 0.7147   |
| 0.8662        | 14.0  | 2289  | 0.7590          | 0.7225   |
| 0.8561        | 15.0  | 2452  | 0.7574          | 0.7199   |
| 0.8234        | 16.0  | 2616  | 0.7457          | 0.7238   |
| 0.844         | 17.0  | 2779  | 0.7416          | 0.7255   |
| 0.7908        | 18.0  | 2943  | 0.7485          | 0.7255   |
| 0.809         | 19.0  | 3106  | 0.7428          | 0.7250   |
| 0.7976        | 20.0  | 3270  | 0.7597          | 0.7203   |
| 0.7691        | 21.0  | 3433  | 0.7333          | 0.7345   |
| 0.7408        | 22.0  | 3597  | 0.7362          | 0.7246   |
| 0.7516        | 23.0  | 3760  | 0.7301          | 0.7298   |
| 0.7887        | 24.0  | 3924  | 0.7263          | 0.7332   |
| 0.7475        | 25.0  | 4087  | 0.7301          | 0.7293   |
| 0.7619        | 26.0  | 4251  | 0.7334          | 0.7298   |
| 0.7509        | 27.0  | 4414  | 0.7332          | 0.7345   |
| 0.7212        | 28.0  | 4578  | 0.7301          | 0.7367   |
| 0.7053        | 29.0  | 4741  | 0.7293          | 0.7328   |
| 0.6634        | 30.0  | 4905  | 0.7412          | 0.7298   |
| 0.677         | 31.0  | 5068  | 0.7221          | 0.7375   |
| 0.6453        | 32.0  | 5232  | 0.7281          | 0.7392   |
| 0.6961        | 33.0  | 5395  | 0.7280          | 0.7392   |
| 0.7135        | 34.0  | 5559  | 0.7348          | 0.7362   |
| 0.6871        | 35.0  | 5722  | 0.7334          | 0.7293   |
| 0.6829        | 36.0  | 5886  | 0.7281          | 0.7328   |
| 0.6742        | 37.0  | 6049  | 0.7332          | 0.7354   |
| 0.6167        | 38.0  | 6213  | 0.7274          | 0.7384   |
| 0.665         | 39.0  | 6376  | 0.7322          | 0.7311   |
| 0.6433        | 40.0  | 6540  | 0.7473          | 0.7345   |
| 0.6661        | 41.0  | 6703  | 0.7358          | 0.7341   |
| 0.6424        | 42.0  | 6867  | 0.7413          | 0.7324   |
| 0.6369        | 43.0  | 7030  | 0.7314          | 0.7414   |
| 0.611         | 44.0  | 7194  | 0.7325          | 0.7388   |
| 0.6556        | 45.0  | 7357  | 0.7485          | 0.7354   |
| 0.6524        | 46.0  | 7521  | 0.7434          | 0.7418   |
| 0.6176        | 47.0  | 7684  | 0.7402          | 0.7410   |
| 0.6142        | 48.0  | 7848  | 0.7480          | 0.7315   |
| 0.5968        | 49.0  | 8011  | 0.7457          | 0.7384   |
| 0.6132        | 50.0  | 8175  | 0.7514          | 0.7328   |
| 0.592         | 51.0  | 8338  | 0.7500          | 0.7375   |
| 0.6347        | 52.0  | 8502  | 0.7533          | 0.7345   |
| 0.5976        | 53.0  | 8665  | 0.7539          | 0.7324   |
| 0.5496        | 54.0  | 8829  | 0.7495          | 0.7388   |
| 0.5845        | 55.0  | 8992  | 0.7550          | 0.7367   |
| 0.5624        | 56.0  | 9156  | 0.7606          | 0.7362   |
| 0.5582        | 57.0  | 9319  | 0.7598          | 0.7341   |
| 0.6206        | 58.0  | 9483  | 0.7608          | 0.7345   |
| 0.5647        | 59.0  | 9646  | 0.7578          | 0.7388   |
| 0.6093        | 60.0  | 9810  | 0.7646          | 0.7358   |
| 0.5625        | 61.0  | 9973  | 0.7622          | 0.7388   |
| 0.6114        | 62.0  | 10137 | 0.7702          | 0.7324   |
| 0.5304        | 63.0  | 10300 | 0.7710          | 0.7367   |
| 0.5646        | 64.0  | 10464 | 0.7807          | 0.7298   |
| 0.5774        | 65.0  | 10627 | 0.7793          | 0.7328   |
| 0.5825        | 66.0  | 10791 | 0.7786          | 0.7375   |
| 0.5111        | 67.0  | 10954 | 0.7742          | 0.7380   |
| 0.5849        | 68.0  | 11118 | 0.7779          | 0.7349   |
| 0.5454        | 69.0  | 11281 | 0.7795          | 0.7367   |
| 0.5158        | 70.0  | 11445 | 0.7806          | 0.7345   |
| 0.5576        | 71.0  | 11608 | 0.7903          | 0.7345   |
| 0.5394        | 72.0  | 11772 | 0.7812          | 0.7380   |
| 0.5099        | 73.0  | 11935 | 0.7808          | 0.7354   |
| 0.5209        | 74.0  | 12099 | 0.7851          | 0.7319   |
| 0.5322        | 75.0  | 12262 | 0.7908          | 0.7401   |
| 0.5351        | 76.0  | 12426 | 0.7960          | 0.7306   |
| 0.5272        | 77.0  | 12589 | 0.7924          | 0.7324   |
| 0.477         | 78.0  | 12753 | 0.7981          | 0.7332   |
| 0.5186        | 79.0  | 12916 | 0.7942          | 0.7341   |
| 0.5366        | 80.0  | 13080 | 0.8016          | 0.7367   |
| 0.4809        | 81.0  | 13243 | 0.8014          | 0.7341   |
| 0.4889        | 82.0  | 13407 | 0.8008          | 0.7354   |
| 0.5287        | 83.0  | 13570 | 0.8010          | 0.7349   |
| 0.4926        | 84.0  | 13734 | 0.8047          | 0.7371   |
| 0.4989        | 85.0  | 13897 | 0.8046          | 0.7384   |
| 0.5483        | 86.0  | 14061 | 0.8022          | 0.7371   |
| 0.5157        | 87.0  | 14224 | 0.8055          | 0.7358   |
| 0.4999        | 88.0  | 14388 | 0.8071          | 0.7319   |
| 0.519         | 89.0  | 14551 | 0.8083          | 0.7362   |
| 0.4534        | 90.0  | 14715 | 0.8082          | 0.7384   |
| 0.429         | 91.0  | 14878 | 0.8103          | 0.7354   |
| 0.5073        | 92.0  | 15042 | 0.8116          | 0.7336   |
| 0.5358        | 93.0  | 15205 | 0.8106          | 0.7341   |
| 0.5049        | 94.0  | 15369 | 0.8111          | 0.7315   |
| 0.4745        | 95.0  | 15532 | 0.8118          | 0.7336   |
| 0.5052        | 96.0  | 15696 | 0.8104          | 0.7371   |
| 0.495         | 97.0  | 15859 | 0.8101          | 0.7354   |
| 0.4752        | 98.0  | 16023 | 0.8117          | 0.7349   |
| 0.4927        | 99.0  | 16186 | 0.8120          | 0.7336   |
| 0.4875        | 99.69 | 16300 | 0.8122          | 0.7345   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0

anger contempt disgust fear happy neutral sad surprise