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# test-hasy-5

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 HASY dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6861
- Accuracy: 0.8067

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1787
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 3.9645        | 1.0   | 541   | 3.4295          | 0.3971   |
| 3.4258        | 2.0   | 1082  | 2.8790          | 0.4782   |
| 3.04          | 3.0   | 1623  | 2.4893          | 0.5468   |
| 2.793         | 4.0   | 2164  | 2.2006          | 0.5738   |
| 2.5551        | 5.0   | 2705  | 1.9056          | 0.6341   |
| 2.3662        | 6.0   | 3246  | 1.7023          | 0.6632   |
| 2.1965        | 7.0   | 3787  | 1.5740          | 0.6798   |
| 2.1397        | 8.0   | 4328  | 1.4561          | 0.6944   |
| 1.9955        | 9.0   | 4869  | 1.3203          | 0.7235   |
| 1.9282        | 10.0  | 5410  | 1.2246          | 0.7380   |
| 1.8368        | 11.0  | 5951  | 1.1823          | 0.7380   |
| 1.812         | 12.0  | 6492  | 1.1298          | 0.7214   |
| 1.7195        | 13.0  | 7033  | 1.0423          | 0.7484   |
| 1.6314        | 14.0  | 7574  | 1.0077          | 0.7422   |
| 1.5979        | 15.0  | 8115  | 1.0051          | 0.7464   |
| 1.5656        | 16.0  | 8656  | 0.9325          | 0.7692   |
| 1.5414        | 17.0  | 9197  | 0.8889          | 0.7734   |
| 1.5342        | 18.0  | 9738  | 0.9073          | 0.7484   |
| 1.4898        | 19.0  | 10279 | 0.8426          | 0.7713   |
| 1.4731        | 20.0  | 10820 | 0.8625          | 0.7443   |
| 1.451         | 21.0  | 11361 | 0.8015          | 0.7630   |
| 1.4578        | 22.0  | 11902 | 0.8520          | 0.7588   |
| 1.4126        | 23.0  | 12443 | 0.7928          | 0.7713   |
| 1.3626        | 24.0  | 12984 | 0.7544          | 0.7838   |
| 1.3694        | 25.0  | 13525 | 0.7699          | 0.7775   |
| 1.3612        | 26.0  | 14066 | 0.7602          | 0.7775   |
| 1.2963        | 27.0  | 14607 | 0.7532          | 0.7713   |
| 1.3009        | 28.0  | 15148 | 0.7013          | 0.7921   |
| 1.2598        | 29.0  | 15689 | 0.7085          | 0.7796   |
| 1.2565        | 30.0  | 16230 | 0.7023          | 0.7775   |
| 1.2735        | 31.0  | 16771 | 0.7048          | 0.7775   |
| 1.2743        | 32.0  | 17312 | 0.6794          | 0.7921   |
| 1.2441        | 33.0  | 17853 | 0.6932          | 0.7859   |
| 1.2282        | 34.0  | 18394 | 0.7039          | 0.7942   |
| 1.2204        | 35.0  | 18935 | 0.6861          | 0.8067   |
| 1.1808        | 36.0  | 19476 | 0.6590          | 0.7963   |
| 1.1928        | 37.0  | 20017 | 0.6784          | 0.7817   |
| 1.1914        | 38.0  | 20558 | 0.6559          | 0.7963   |
| 1.1856        | 39.0  | 21099 | 0.6769          | 0.7963   |
| 1.1585        | 40.0  | 21640 | 0.6498          | 0.8004   |
| 1.1713        | 41.0  | 22181 | 0.6447          | 0.7921   |
| 1.1183        | 42.0  | 22722 | 0.6748          | 0.7713   |
| 1.1564        | 43.0  | 23263 | 0.6545          | 0.7921   |
| 1.1215        | 44.0  | 23804 | 0.6690          | 0.7879   |
| 1.1008        | 45.0  | 24345 | 0.6598          | 0.7879   |
| 1.1344        | 46.0  | 24886 | 0.6550          | 0.8025   |
| 1.126         | 47.0  | 25427 | 0.6521          | 0.7859   |
| 1.125         | 48.0  | 25968 | 0.6813          | 0.7817   |
| 1.0855        | 49.0  | 26509 | 0.6419          | 0.7859   |
| 1.0452        | 50.0  | 27050 | 0.6551          | 0.8004   |
| 1.0626        | 51.0  | 27591 | 0.6675          | 0.7921   |
| 1.0155        | 52.0  | 28132 | 0.6946          | 0.7921   |
| 1.0319        | 53.0  | 28673 | 0.6942          | 0.7796   |
| 1.0488        | 54.0  | 29214 | 0.6496          | 0.7983   |
| 1.0558        | 55.0  | 29755 | 0.6465          | 0.8046   |
| 0.9913        | 56.0  | 30296 | 0.6654          | 0.7921   |
| 1.0555        | 57.0  | 30837 | 0.6561          | 0.7963   |
| 0.9803        | 58.0  | 31378 | 0.6732          | 0.7942   |
| 1.0393        | 59.0  | 31919 | 0.6893          | 0.7817   |
| 0.9677        | 60.0  | 32460 | 0.6824          | 0.8046   |
| 1.0082        | 61.0  | 33001 | 0.6618          | 0.7942   |
| 1.0096        | 62.0  | 33542 | 0.6691          | 0.7838   |
| 0.9685        | 63.0  | 34083 | 0.6793          | 0.8025   |
| 0.9847        | 64.0  | 34624 | 0.6895          | 0.7838   |
| 0.9639        | 65.0  | 35165 | 0.7297          | 0.7734   |
| 0.9776        | 66.0  | 35706 | 0.6561          | 0.7921   |
| 1.0074        | 67.0  | 36247 | 0.6999          | 0.7775   |
| 0.9466        | 68.0  | 36788 | 0.6881          | 0.7942   |
| 0.9425        | 69.0  | 37329 | 0.6806          | 0.7963   |
| 0.9594        | 70.0  | 37870 | 0.7202          | 0.7900   |
| 0.9311        | 71.0  | 38411 | 0.7162          | 0.7755   |
| 0.9429        | 72.0  | 38952 | 0.7284          | 0.7921   |
| 0.9666        | 73.0  | 39493 | 0.6871          | 0.7963   |
| 0.945         | 74.0  | 40034 | 0.6779          | 0.7942   |
| 0.9387        | 75.0  | 40575 | 0.7358          | 0.7942   |
| 0.9132        | 76.0  | 41116 | 0.7044          | 0.7942   |
| 0.9181        | 77.0  | 41657 | 0.7041          | 0.7963   |
| 0.9218        | 78.0  | 42198 | 0.6986          | 0.7942   |
| 0.8621        | 79.0  | 42739 | 0.6909          | 0.8004   |
| 0.9236        | 80.0  | 43280 | 0.7136          | 0.7983   |
| 0.8667        | 81.0  | 43821 | 0.7009          | 0.8025   |
| 0.8856        | 82.0  | 44362 | 0.7128          | 0.7921   |
| 0.917         | 83.0  | 44903 | 0.7135          | 0.7983   |
| 0.8835        | 84.0  | 45444 | 0.7295          | 0.7900   |
| 0.8879        | 85.0  | 45985 | 0.7450          | 0.7900   |
| 0.8764        | 86.0  | 46526 | 0.7362          | 0.7942   |
| 0.8674        | 87.0  | 47067 | 0.7232          | 0.7942   |
| 0.8583        | 88.0  | 47608 | 0.7408          | 0.7942   |
| 0.881         | 89.0  | 48149 | 0.7378          | 0.8004   |
| 0.8668        | 90.0  | 48690 | 0.7473          | 0.7900   |
| 0.8779        | 91.0  | 49231 | 0.7438          | 0.7983   |
| 0.8717        | 92.0  | 49772 | 0.7390          | 0.8004   |
| 0.8781        | 93.0  | 50313 | 0.7474          | 0.7983   |
| 0.8845        | 94.0  | 50854 | 0.7446          | 0.7900   |
| 0.8623        | 95.0  | 51395 | 0.7316          | 0.7921   |
| 0.8341        | 96.0  | 51936 | 0.7457          | 0.7879   |
| 0.8766        | 97.0  | 52477 | 0.7436          | 0.7921   |
| 0.8681        | 98.0  | 53018 | 0.7484          | 0.7900   |
| 0.8635        | 99.0  | 53559 | 0.7392          | 0.7942   |
| 0.8091        | 100.0 | 54100 | 0.7391          | 0.7921   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

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