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# vit-msn-base-finetuned-lf-invalidation

This model is a fine-tuned version of [facebook/vit-msn-base](https://huggingface.co/facebook/vit-msn-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2414
- Accuracy: 0.9234

## 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: 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: 80

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.96  | 6    | 0.6512          | 0.6957   |
| 0.7053        | 1.92  | 12   | 0.6311          | 0.6809   |
| 0.7053        | 2.88  | 18   | 0.5361          | 0.7277   |
| 0.5163        | 4.0   | 25   | 0.3341          | 0.8681   |
| 0.3242        | 4.96  | 31   | 0.3167          | 0.8809   |
| 0.3242        | 5.92  | 37   | 0.3960          | 0.8191   |
| 0.2779        | 6.88  | 43   | 0.3818          | 0.8255   |
| 0.2348        | 8.0   | 50   | 0.5019          | 0.7362   |
| 0.2348        | 8.96  | 56   | 0.2944          | 0.8851   |
| 0.26          | 9.92  | 62   | 0.2414          | 0.9234   |
| 0.26          | 10.88 | 68   | 0.3664          | 0.8298   |
| 0.2778        | 12.0  | 75   | 0.2505          | 0.9043   |
| 0.2271        | 12.96 | 81   | 0.6277          | 0.6298   |
| 0.2271        | 13.92 | 87   | 0.2753          | 0.8745   |
| 0.2488        | 14.88 | 93   | 0.6249          | 0.6957   |
| 0.2729        | 16.0  | 100  | 0.5195          | 0.7149   |
| 0.2729        | 16.96 | 106  | 0.7984          | 0.5745   |
| 0.3261        | 17.92 | 112  | 0.4631          | 0.7723   |
| 0.3261        | 18.88 | 118  | 1.1010          | 0.5149   |
| 0.2212        | 20.0  | 125  | 0.2337          | 0.9170   |
| 0.2802        | 20.96 | 131  | 0.4638          | 0.7574   |
| 0.2802        | 21.92 | 137  | 0.3859          | 0.8362   |
| 0.2112        | 22.88 | 143  | 0.6708          | 0.6894   |
| 0.2231        | 24.0  | 150  | 0.3387          | 0.8681   |
| 0.2231        | 24.96 | 156  | 0.7045          | 0.6553   |
| 0.2037        | 25.92 | 162  | 0.3958          | 0.8277   |
| 0.2037        | 26.88 | 168  | 0.5082          | 0.7702   |
| 0.1845        | 28.0  | 175  | 0.5991          | 0.7234   |
| 0.1898        | 28.96 | 181  | 0.5108          | 0.7617   |
| 0.1898        | 29.92 | 187  | 0.2720          | 0.9085   |
| 0.2118        | 30.88 | 193  | 0.4936          | 0.7851   |
| 0.2097        | 32.0  | 200  | 0.3748          | 0.8404   |
| 0.2097        | 32.96 | 206  | 0.5048          | 0.7766   |
| 0.1704        | 33.92 | 212  | 0.4368          | 0.7957   |
| 0.1704        | 34.88 | 218  | 0.6959          | 0.6830   |
| 0.1962        | 36.0  | 225  | 1.0097          | 0.5957   |
| 0.1686        | 36.96 | 231  | 0.4992          | 0.7915   |
| 0.1686        | 37.92 | 237  | 0.5374          | 0.7574   |
| 0.1855        | 38.88 | 243  | 0.3710          | 0.8340   |
| 0.1528        | 40.0  | 250  | 0.3631          | 0.8447   |
| 0.1528        | 40.96 | 256  | 0.5589          | 0.7681   |
| 0.1523        | 41.92 | 262  | 0.5147          | 0.7809   |
| 0.1523        | 42.88 | 268  | 0.5299          | 0.7638   |
| 0.1709        | 44.0  | 275  | 0.5937          | 0.7447   |
| 0.1527        | 44.96 | 281  | 0.5969          | 0.7383   |
| 0.1527        | 45.92 | 287  | 0.6439          | 0.7255   |
| 0.1397        | 46.88 | 293  | 0.7721          | 0.6723   |
| 0.1538        | 48.0  | 300  | 0.5768          | 0.7702   |
| 0.1538        | 48.96 | 306  | 0.5801          | 0.7596   |
| 0.1466        | 49.92 | 312  | 0.5673          | 0.7574   |
| 0.1466        | 50.88 | 318  | 0.6469          | 0.7085   |
| 0.1302        | 52.0  | 325  | 0.7276          | 0.6957   |
| 0.1565        | 52.96 | 331  | 0.8247          | 0.6723   |
| 0.1565        | 53.92 | 337  | 0.4811          | 0.7979   |
| 0.1267        | 54.88 | 343  | 0.6373          | 0.7021   |
| 0.1424        | 56.0  | 350  | 0.7252          | 0.6723   |
| 0.1424        | 56.96 | 356  | 0.5697          | 0.7489   |
| 0.1053        | 57.92 | 362  | 0.7067          | 0.6957   |
| 0.1053        | 58.88 | 368  | 0.6577          | 0.7064   |
| 0.1301        | 60.0  | 375  | 0.5326          | 0.7745   |
| 0.0906        | 60.96 | 381  | 0.5468          | 0.7851   |
| 0.0906        | 61.92 | 387  | 0.4413          | 0.8277   |
| 0.0974        | 62.88 | 393  | 0.5479          | 0.7660   |
| 0.1133        | 64.0  | 400  | 0.7109          | 0.7043   |
| 0.1133        | 64.96 | 406  | 0.5735          | 0.7617   |
| 0.1189        | 65.92 | 412  | 0.4084          | 0.8298   |
| 0.1189        | 66.88 | 418  | 0.5716          | 0.7489   |
| 0.1064        | 68.0  | 425  | 0.5537          | 0.7553   |
| 0.1084        | 68.96 | 431  | 0.4569          | 0.8021   |
| 0.1084        | 69.92 | 437  | 0.5227          | 0.7617   |
| 0.1054        | 70.88 | 443  | 0.5995          | 0.7277   |
| 0.1005        | 72.0  | 450  | 0.5560          | 0.7638   |
| 0.1005        | 72.96 | 456  | 0.4550          | 0.8064   |
| 0.1028        | 73.92 | 462  | 0.4404          | 0.8234   |
| 0.1028        | 74.88 | 468  | 0.4761          | 0.7957   |
| 0.0917        | 76.0  | 475  | 0.5278          | 0.7681   |
| 0.1009        | 76.8  | 480  | 0.5346          | 0.7617   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1

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