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# sashes_model

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: 0.3784
- Accuracy: 0.8760

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 112

### Training results

| Training Loss | Epoch    | Step | Validation Loss | Accuracy |
|:-------------:|:--------:|:----:|:---------------:|:--------:|
| No log        | 0.9697   | 8    | 2.2973          | 0.1434   |
| 2.2994        | 1.9394   | 16   | 2.2717          | 0.1957   |
| 2.2791        | 2.9091   | 24   | 2.2377          | 0.2287   |
| 2.2378        | 4.0      | 33   | 2.1866          | 0.3178   |
| 2.1604        | 4.9697   | 41   | 2.1096          | 0.3934   |
| 2.1604        | 5.9394   | 49   | 2.0257          | 0.4322   |
| 2.0801        | 6.9091   | 57   | 1.9312          | 0.4264   |
| 1.9587        | 8.0      | 66   | 1.7939          | 0.4942   |
| 1.821         | 8.9697   | 74   | 1.6869          | 0.5465   |
| 1.6903        | 9.9394   | 82   | 1.6025          | 0.5736   |
| 1.5687        | 10.9091  | 90   | 1.4849          | 0.6202   |
| 1.5687        | 12.0     | 99   | 1.4674          | 0.5407   |
| 1.4183        | 12.9697  | 107  | 1.3539          | 0.6163   |
| 1.3907        | 13.9394  | 115  | 1.2365          | 0.6938   |
| 1.3058        | 14.9091  | 123  | 1.2258          | 0.6938   |
| 1.2181        | 16.0     | 132  | 1.1759          | 0.6822   |
| 1.1537        | 16.9697  | 140  | 1.1413          | 0.7074   |
| 1.1537        | 17.9394  | 148  | 1.0586          | 0.7248   |
| 1.0819        | 18.9091  | 156  | 1.0059          | 0.7558   |
| 0.9905        | 20.0     | 165  | 0.9575          | 0.7578   |
| 1.0055        | 20.9697  | 173  | 0.9807          | 0.7442   |
| 0.9484        | 21.9394  | 181  | 0.9553          | 0.7539   |
| 0.9484        | 22.9091  | 189  | 0.8213          | 0.8004   |
| 0.8974        | 24.0     | 198  | 0.8305          | 0.8043   |
| 0.8545        | 24.9697  | 206  | 0.8273          | 0.7849   |
| 0.8724        | 25.9394  | 214  | 0.8177          | 0.7519   |
| 0.8642        | 26.9091  | 222  | 0.7692          | 0.7926   |
| 0.7609        | 28.0     | 231  | 0.7293          | 0.8062   |
| 0.7609        | 28.9697  | 239  | 0.7001          | 0.8198   |
| 0.7418        | 29.9394  | 247  | 0.7899          | 0.7636   |
| 0.7552        | 30.9091  | 255  | 0.6595          | 0.8101   |
| 0.7291        | 32.0     | 264  | 0.6971          | 0.7907   |
| 0.693         | 32.9697  | 272  | 0.7215          | 0.7946   |
| 0.6891        | 33.9394  | 280  | 0.6980          | 0.8004   |
| 0.6891        | 34.9091  | 288  | 0.6200          | 0.8372   |
| 0.6936        | 36.0     | 297  | 0.7245          | 0.7733   |
| 0.6698        | 36.9697  | 305  | 0.6724          | 0.7984   |
| 0.6502        | 37.9394  | 313  | 0.6701          | 0.8023   |
| 0.6988        | 38.9091  | 321  | 0.6049          | 0.8236   |
| 0.6709        | 40.0     | 330  | 0.6397          | 0.7965   |
| 0.6709        | 40.9697  | 338  | 0.5654          | 0.8391   |
| 0.652         | 41.9394  | 346  | 0.6371          | 0.8101   |
| 0.64          | 42.9091  | 354  | 0.6341          | 0.8062   |
| 0.6368        | 44.0     | 363  | 0.5662          | 0.8527   |
| 0.595         | 44.9697  | 371  | 0.5744          | 0.8411   |
| 0.595         | 45.9394  | 379  | 0.5465          | 0.8430   |
| 0.5823        | 46.9091  | 387  | 0.6254          | 0.7984   |
| 0.5514        | 48.0     | 396  | 0.5368          | 0.8333   |
| 0.5693        | 48.9697  | 404  | 0.5705          | 0.8043   |
| 0.5244        | 49.9394  | 412  | 0.5685          | 0.8314   |
| 0.5495        | 50.9091  | 420  | 0.5811          | 0.8120   |
| 0.5495        | 52.0     | 429  | 0.5037          | 0.8469   |
| 0.5501        | 52.9697  | 437  | 0.5423          | 0.8372   |
| 0.5405        | 53.9394  | 445  | 0.5487          | 0.8178   |
| 0.534         | 54.9091  | 453  | 0.5607          | 0.8217   |
| 0.5502        | 56.0     | 462  | 0.5141          | 0.8198   |
| 0.4772        | 56.9697  | 470  | 0.4813          | 0.8605   |
| 0.4772        | 57.9394  | 478  | 0.5007          | 0.8566   |
| 0.4823        | 58.9091  | 486  | 0.4847          | 0.8624   |
| 0.5107        | 60.0     | 495  | 0.5273          | 0.8333   |
| 0.5205        | 60.9697  | 503  | 0.4981          | 0.8430   |
| 0.5171        | 61.9394  | 511  | 0.4819          | 0.8430   |
| 0.5171        | 62.9091  | 519  | 0.4415          | 0.8682   |
| 0.5498        | 64.0     | 528  | 0.4578          | 0.8566   |
| 0.4732        | 64.9697  | 536  | 0.4614          | 0.8450   |
| 0.4623        | 65.9394  | 544  | 0.4923          | 0.8488   |
| 0.4406        | 66.9091  | 552  | 0.4556          | 0.8547   |
| 0.4889        | 68.0     | 561  | 0.4727          | 0.8488   |
| 0.4889        | 68.9697  | 569  | 0.4746          | 0.8469   |
| 0.4532        | 69.9394  | 577  | 0.4496          | 0.8585   |
| 0.3988        | 70.9091  | 585  | 0.4260          | 0.8702   |
| 0.4608        | 72.0     | 594  | 0.4464          | 0.8547   |
| 0.4429        | 72.9697  | 602  | 0.3946          | 0.8818   |
| 0.4502        | 73.9394  | 610  | 0.4566          | 0.8527   |
| 0.4502        | 74.9091  | 618  | 0.4472          | 0.8663   |
| 0.4381        | 76.0     | 627  | 0.4701          | 0.8372   |
| 0.4437        | 76.9697  | 635  | 0.4351          | 0.8488   |
| 0.4223        | 77.9394  | 643  | 0.4011          | 0.8779   |
| 0.4121        | 78.9091  | 651  | 0.4328          | 0.8547   |
| 0.4164        | 80.0     | 660  | 0.3908          | 0.8857   |
| 0.4164        | 80.9697  | 668  | 0.3774          | 0.8876   |
| 0.418         | 81.9394  | 676  | 0.4397          | 0.8643   |
| 0.3961        | 82.9091  | 684  | 0.4500          | 0.8585   |
| 0.4035        | 84.0     | 693  | 0.3968          | 0.8624   |
| 0.4269        | 84.9697  | 701  | 0.4457          | 0.8566   |
| 0.4269        | 85.9394  | 709  | 0.3987          | 0.8740   |
| 0.3694        | 86.9091  | 717  | 0.4074          | 0.8760   |
| 0.3642        | 88.0     | 726  | 0.3781          | 0.9012   |
| 0.3985        | 88.9697  | 734  | 0.3575          | 0.8934   |
| 0.4237        | 89.9394  | 742  | 0.4313          | 0.8508   |
| 0.4156        | 90.9091  | 750  | 0.3504          | 0.8934   |
| 0.4156        | 92.0     | 759  | 0.4116          | 0.8566   |
| 0.389         | 92.9697  | 767  | 0.3739          | 0.8779   |
| 0.3934        | 93.9394  | 775  | 0.3990          | 0.8779   |
| 0.4231        | 94.9091  | 783  | 0.4164          | 0.8624   |
| 0.3792        | 96.0     | 792  | 0.3808          | 0.8721   |
| 0.3928        | 96.9697  | 800  | 0.3534          | 0.8915   |
| 0.3928        | 97.9394  | 808  | 0.3643          | 0.8798   |
| 0.4003        | 98.9091  | 816  | 0.4150          | 0.8624   |
| 0.3929        | 100.0    | 825  | 0.3477          | 0.9050   |
| 0.3992        | 100.9697 | 833  | 0.4037          | 0.8682   |
| 0.387         | 101.9394 | 841  | 0.3453          | 0.9050   |
| 0.387         | 102.9091 | 849  | 0.4012          | 0.8682   |
| 0.3942        | 104.0    | 858  | 0.3843          | 0.8915   |
| 0.3794        | 104.9697 | 866  | 0.3478          | 0.8798   |
| 0.3794        | 105.9394 | 874  | 0.3111          | 0.9167   |
| 0.396         | 106.9091 | 882  | 0.3588          | 0.8818   |
| 0.3767        | 108.0    | 891  | 0.3602          | 0.8837   |
| 0.3767        | 108.6061 | 896  | 0.3784          | 0.8760   |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1

2 sashes fix bottom one field 2 sashes fix bottom two fields 2 sashes fix bottom two fields monoblock 2 sashes fix_bottom one field monoblock 2 sashes monoblock 2_sashes 3 sashes monoblock 3_sashes 4 sashes monoblock 4_sashes