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# square_run_second_vote_full_pic_75

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4672
- F1 Macro: 0.3451
- F1 Micro: 0.4545
- F1 Weighted: 0.4226
- Precision Macro: 0.3766
- Precision Micro: 0.4545
- Precision Weighted: 0.4564
- Recall Macro: 0.3721
- Recall Micro: 0.4545
- Recall Weighted: 0.4545
- Accuracy: 0.4545

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | F1 Weighted | Precision Macro | Precision Micro | Precision Weighted | Recall Macro | Recall Micro | Recall Weighted | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.7944        | 1.0   | 58   | 1.8560          | 0.1268   | 0.2424   | 0.1788      | 0.1005          | 0.2424          | 0.1425             | 0.1734       | 0.2424       | 0.2424          | 0.2424   |
| 1.8983        | 2.0   | 116  | 1.8865          | 0.1533   | 0.2197   | 0.1705      | 0.1708          | 0.2197          | 0.1813             | 0.1858       | 0.2197       | 0.2197          | 0.2197   |
| 1.4428        | 3.0   | 174  | 1.8035          | 0.2270   | 0.3030   | 0.2733      | 0.2740          | 0.3030          | 0.3193             | 0.2427       | 0.3030       | 0.3030          | 0.3030   |
| 1.5854        | 4.0   | 232  | 1.7041          | 0.2293   | 0.3333   | 0.2630      | 0.2292          | 0.3333          | 0.2516             | 0.2763       | 0.3333       | 0.3333          | 0.3333   |
| 1.644         | 5.0   | 290  | 1.6834          | 0.2759   | 0.4242   | 0.3362      | 0.2453          | 0.4242          | 0.3082             | 0.3601       | 0.4242       | 0.4242          | 0.4242   |
| 1.7673        | 6.0   | 348  | 1.7508          | 0.2257   | 0.3333   | 0.2896      | 0.2153          | 0.3333          | 0.2776             | 0.2611       | 0.3333       | 0.3333          | 0.3333   |
| 1.8791        | 7.0   | 406  | 1.6072          | 0.3200   | 0.4167   | 0.3759      | 0.3171          | 0.4167          | 0.3666             | 0.3494       | 0.4167       | 0.4167          | 0.4167   |
| 1.3323        | 8.0   | 464  | 1.6554          | 0.3265   | 0.4015   | 0.3794      | 0.3576          | 0.4015          | 0.4045             | 0.3323       | 0.4015       | 0.4015          | 0.4015   |
| 1.7047        | 9.0   | 522  | 1.7295          | 0.3186   | 0.3864   | 0.3716      | 0.3364          | 0.3864          | 0.3884             | 0.3275       | 0.3864       | 0.3864          | 0.3864   |
| 1.1897        | 10.0  | 580  | 1.7238          | 0.2782   | 0.3561   | 0.3224      | 0.2981          | 0.3561          | 0.3401             | 0.3110       | 0.3561       | 0.3561          | 0.3561   |
| 0.8908        | 11.0  | 638  | 2.1481          | 0.2774   | 0.3333   | 0.3266      | 0.3840          | 0.3333          | 0.4285             | 0.2900       | 0.3333       | 0.3333          | 0.3333   |
| 0.4492        | 12.0  | 696  | 1.9300          | 0.2862   | 0.3636   | 0.3330      | 0.3623          | 0.3636          | 0.3947             | 0.2959       | 0.3636       | 0.3636          | 0.3636   |
| 0.6555        | 13.0  | 754  | 1.8931          | 0.3053   | 0.3788   | 0.3670      | 0.3329          | 0.3788          | 0.3854             | 0.3084       | 0.3788       | 0.3788          | 0.3788   |
| 0.3586        | 14.0  | 812  | 2.0316          | 0.3475   | 0.4242   | 0.4103      | 0.3722          | 0.4242          | 0.4360             | 0.3616       | 0.4242       | 0.4242          | 0.4242   |
| 0.6805        | 15.0  | 870  | 2.0638          | 0.3389   | 0.4091   | 0.3917      | 0.3613          | 0.4091          | 0.4150             | 0.3572       | 0.4091       | 0.4091          | 0.4091   |
| 0.8902        | 16.0  | 928  | 2.2817          | 0.2992   | 0.3636   | 0.3466      | 0.3388          | 0.3636          | 0.4092             | 0.3156       | 0.3636       | 0.3636          | 0.3636   |
| 0.3393        | 17.0  | 986  | 2.4104          | 0.3031   | 0.3485   | 0.3527      | 0.3116          | 0.3485          | 0.3658             | 0.3015       | 0.3485       | 0.3485          | 0.3485   |
| 0.2469        | 18.0  | 1044 | 2.4341          | 0.3373   | 0.3939   | 0.3980      | 0.3472          | 0.3939          | 0.4130             | 0.3381       | 0.3939       | 0.3939          | 0.3939   |
| 0.0485        | 19.0  | 1102 | 2.5798          | 0.3454   | 0.4015   | 0.3908      | 0.3639          | 0.4015          | 0.4075             | 0.3545       | 0.4015       | 0.4015          | 0.4015   |
| 0.0693        | 20.0  | 1160 | 2.4961          | 0.3781   | 0.4470   | 0.4350      | 0.4173          | 0.4470          | 0.4664             | 0.3837       | 0.4470       | 0.4470          | 0.4470   |
| 0.0095        | 21.0  | 1218 | 2.7183          | 0.3840   | 0.4621   | 0.4510      | 0.4268          | 0.4621          | 0.4916             | 0.3868       | 0.4621       | 0.4621          | 0.4621   |
| 0.0078        | 22.0  | 1276 | 2.7620          | 0.3520   | 0.4091   | 0.4091      | 0.3617          | 0.4091          | 0.4199             | 0.3528       | 0.4091       | 0.4091          | 0.4091   |
| 0.0083        | 23.0  | 1334 | 2.8349          | 0.3507   | 0.4167   | 0.4058      | 0.3826          | 0.4167          | 0.4324             | 0.3560       | 0.4167       | 0.4167          | 0.4167   |
| 0.0024        | 24.0  | 1392 | 2.7839          | 0.3630   | 0.4167   | 0.4169      | 0.4041          | 0.4167          | 0.4515             | 0.3594       | 0.4167       | 0.4167          | 0.4167   |
| 0.0107        | 25.0  | 1450 | 2.8616          | 0.3600   | 0.4242   | 0.4212      | 0.3597          | 0.4242          | 0.4236             | 0.3653       | 0.4242       | 0.4242          | 0.4242   |
| 0.0014        | 26.0  | 1508 | 2.9104          | 0.3790   | 0.4394   | 0.4343      | 0.3870          | 0.4394          | 0.4386             | 0.3810       | 0.4394       | 0.4394          | 0.4394   |
| 0.0021        | 27.0  | 1566 | 2.9763          | 0.3778   | 0.4394   | 0.4342      | 0.3989          | 0.4394          | 0.4515             | 0.3785       | 0.4394       | 0.4394          | 0.4394   |
| 0.0106        | 28.0  | 1624 | 2.9525          | 0.3866   | 0.4545   | 0.4479      | 0.3957          | 0.4545          | 0.4533             | 0.3892       | 0.4545       | 0.4545          | 0.4545   |
| 0.0044        | 29.0  | 1682 | 2.9417          | 0.3862   | 0.4545   | 0.4438      | 0.3911          | 0.4545          | 0.4447             | 0.3930       | 0.4545       | 0.4545          | 0.4545   |
| 0.0019        | 30.0  | 1740 | 2.9399          | 0.3876   | 0.4545   | 0.4461      | 0.3937          | 0.4545          | 0.4485             | 0.3928       | 0.4545       | 0.4545          | 0.4545   |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0

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