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# square_run_first_vote_full_pic_50

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: 1.8395
- F1 Macro: 0.2931
- F1 Micro: 0.3182
- F1 Weighted: 0.3126
- Precision Macro: 0.3510
- Precision Micro: 0.3182
- Precision Weighted: 0.3702
- Recall Macro: 0.2978
- Recall Micro: 0.3182
- Recall Weighted: 0.3182
- Accuracy: 0.3182

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.9357        | 1.0   | 58   | 1.9251          | 0.1142   | 0.2045   | 0.1326      | 0.2916          | 0.2045          | 0.3719             | 0.1805       | 0.2045       | 0.2045          | 0.2045   |
| 1.8428        | 2.0   | 116  | 1.9975          | 0.0864   | 0.1894   | 0.0941      | 0.0690          | 0.1894          | 0.0756             | 0.1762       | 0.1894       | 0.1894          | 0.1894   |
| 1.8324        | 3.0   | 174  | 1.8298          | 0.1725   | 0.2727   | 0.2132      | 0.1532          | 0.2727          | 0.1894             | 0.2191       | 0.2727       | 0.2727          | 0.2727   |
| 1.5574        | 4.0   | 232  | 1.7557          | 0.1128   | 0.2652   | 0.1548      | 0.0798          | 0.2652          | 0.1095             | 0.1928       | 0.2652       | 0.2652          | 0.2652   |
| 1.9434        | 5.0   | 290  | 1.7244          | 0.3019   | 0.3939   | 0.3572      | 0.2839          | 0.3939          | 0.3404             | 0.3375       | 0.3939       | 0.3939          | 0.3939   |
| 1.9357        | 6.0   | 348  | 1.6611          | 0.3208   | 0.3712   | 0.3669      | 0.3285          | 0.3712          | 0.3749             | 0.3253       | 0.3712       | 0.3712          | 0.3712   |
| 1.8454        | 7.0   | 406  | 1.6835          | 0.3043   | 0.3939   | 0.3397      | 0.3472          | 0.3939          | 0.3939             | 0.3515       | 0.3939       | 0.3939          | 0.3939   |
| 1.7616        | 8.0   | 464  | 1.8893          | 0.2312   | 0.2803   | 0.2544      | 0.3179          | 0.2803          | 0.3702             | 0.2728       | 0.2803       | 0.2803          | 0.2803   |
| 1.5512        | 9.0   | 522  | 1.7856          | 0.2366   | 0.3182   | 0.2696      | 0.2788          | 0.3182          | 0.3172             | 0.2820       | 0.3182       | 0.3182          | 0.3182   |
| 1.777         | 10.0  | 580  | 1.9182          | 0.3136   | 0.3864   | 0.3465      | 0.3176          | 0.3864          | 0.3434             | 0.3525       | 0.3864       | 0.3864          | 0.3864   |
| 1.3075        | 11.0  | 638  | 1.7205          | 0.3324   | 0.3939   | 0.3795      | 0.3461          | 0.3939          | 0.3893             | 0.3407       | 0.3939       | 0.3939          | 0.3939   |
| 0.8476        | 12.0  | 696  | 1.8083          | 0.3203   | 0.3788   | 0.3495      | 0.3297          | 0.3788          | 0.3672             | 0.3581       | 0.3788       | 0.3788          | 0.3788   |
| 1.0324        | 13.0  | 754  | 1.9825          | 0.3046   | 0.3485   | 0.3341      | 0.3316          | 0.3485          | 0.3807             | 0.3315       | 0.3485       | 0.3485          | 0.3485   |
| 1.154         | 14.0  | 812  | 2.0418          | 0.2869   | 0.3333   | 0.3151      | 0.2847          | 0.3333          | 0.3140             | 0.3064       | 0.3333       | 0.3333          | 0.3333   |
| 0.5406        | 15.0  | 870  | 2.1651          | 0.3242   | 0.3561   | 0.3453      | 0.3366          | 0.3561          | 0.3561             | 0.3313       | 0.3561       | 0.3561          | 0.3561   |
| 1.5052        | 16.0  | 928  | 2.3796          | 0.2814   | 0.3561   | 0.3228      | 0.3189          | 0.3561          | 0.3611             | 0.3127       | 0.3561       | 0.3561          | 0.3561   |
| 0.1641        | 17.0  | 986  | 2.2210          | 0.3286   | 0.3864   | 0.3741      | 0.3346          | 0.3864          | 0.3768             | 0.3361       | 0.3864       | 0.3864          | 0.3864   |
| 0.1201        | 18.0  | 1044 | 2.2744          | 0.3384   | 0.3939   | 0.3852      | 0.3331          | 0.3939          | 0.3811             | 0.3474       | 0.3939       | 0.3939          | 0.3939   |
| 0.1059        | 19.0  | 1102 | 2.4881          | 0.3198   | 0.3712   | 0.3485      | 0.3702          | 0.3712          | 0.3640             | 0.3244       | 0.3712       | 0.3712          | 0.3712   |
| 0.0828        | 20.0  | 1160 | 2.6911          | 0.3369   | 0.4091   | 0.3897      | 0.3378          | 0.4091          | 0.3826             | 0.3473       | 0.4091       | 0.4091          | 0.4091   |
| 0.0903        | 21.0  | 1218 | 2.9249          | 0.3351   | 0.3561   | 0.3564      | 0.3430          | 0.3561          | 0.3614             | 0.3341       | 0.3561       | 0.3561          | 0.3561   |
| 0.0455        | 22.0  | 1276 | 3.1538          | 0.2830   | 0.3409   | 0.3261      | 0.2951          | 0.3409          | 0.3330             | 0.2889       | 0.3409       | 0.3409          | 0.3409   |
| 0.0137        | 23.0  | 1334 | 3.0196          | 0.3147   | 0.3712   | 0.3598      | 0.3095          | 0.3712          | 0.3530             | 0.3246       | 0.3712       | 0.3712          | 0.3712   |
| 0.0088        | 24.0  | 1392 | 3.0033          | 0.3512   | 0.4015   | 0.3958      | 0.3562          | 0.4015          | 0.4024             | 0.3586       | 0.4015       | 0.4015          | 0.4015   |
| 0.205         | 25.0  | 1450 | 3.1499          | 0.3854   | 0.4091   | 0.3978      | 0.3923          | 0.4091          | 0.4032             | 0.3939       | 0.4091       | 0.4091          | 0.4091   |
| 0.0072        | 26.0  | 1508 | 3.2906          | 0.3440   | 0.3712   | 0.3651      | 0.3438          | 0.3712          | 0.3663             | 0.3516       | 0.3712       | 0.3712          | 0.3712   |
| 0.0019        | 27.0  | 1566 | 3.3223          | 0.3542   | 0.3712   | 0.3663      | 0.3524          | 0.3712          | 0.3673             | 0.3627       | 0.3712       | 0.3712          | 0.3712   |
| 0.0043        | 28.0  | 1624 | 3.2986          | 0.3729   | 0.3864   | 0.3840      | 0.3726          | 0.3864          | 0.3838             | 0.3753       | 0.3864       | 0.3864          | 0.3864   |
| 0.0016        | 29.0  | 1682 | 3.3453          | 0.3469   | 0.3788   | 0.3741      | 0.3504          | 0.3788          | 0.3744             | 0.3483       | 0.3788       | 0.3788          | 0.3788   |
| 0.0031        | 30.0  | 1740 | 3.3308          | 0.3465   | 0.3788   | 0.3753      | 0.3514          | 0.3788          | 0.3760             | 0.3456       | 0.3788       | 0.3788          | 0.3788   |


### Framework versions

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

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