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# deit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-14687

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3950
- Accuracy: 0.8310
- Recall: 0.8310
- F1: 0.8298
- Precision: 0.8360

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.8293        | 0.9974  | 293   | 0.7793          | 0.7680   | 0.7680 | 0.7403 | 0.7277    |
| 0.5921        | 1.9983  | 587   | 0.5663          | 0.7940   | 0.7940 | 0.7843 | 0.7839    |
| 0.4308        | 2.9991  | 881   | 0.4589          | 0.8208   | 0.8208 | 0.8161 | 0.8213    |
| 0.3999        | 4.0     | 1175  | 0.4772          | 0.8263   | 0.8263 | 0.8216 | 0.8337    |
| 0.4801        | 4.9974  | 1468  | 0.4258          | 0.8378   | 0.8378 | 0.8306 | 0.8463    |
| 0.4201        | 5.9983  | 1762  | 0.4120          | 0.8246   | 0.8246 | 0.8213 | 0.8394    |
| 0.3233        | 6.9991  | 2056  | 0.3989          | 0.8306   | 0.8306 | 0.8268 | 0.8445    |
| 0.3954        | 8.0     | 2350  | 0.3794          | 0.8365   | 0.8365 | 0.8341 | 0.8383    |
| 0.2835        | 8.9974  | 2643  | 0.4438          | 0.8318   | 0.8318 | 0.8278 | 0.8434    |
| 0.2913        | 9.9983  | 2937  | 0.3799          | 0.8416   | 0.8416 | 0.8404 | 0.8451    |
| 0.3261        | 10.9991 | 3231  | 0.3694          | 0.8297   | 0.8297 | 0.8272 | 0.8306    |
| 0.3299        | 12.0    | 3525  | 0.3637          | 0.8442   | 0.8442 | 0.8425 | 0.8529    |
| 0.3273        | 12.9974 | 3818  | 0.3649          | 0.8421   | 0.8421 | 0.8411 | 0.8482    |
| 0.2596        | 13.9983 | 4112  | 0.4152          | 0.8259   | 0.8259 | 0.8213 | 0.8281    |
| 0.2813        | 14.9991 | 4406  | 0.3578          | 0.8429   | 0.8429 | 0.8409 | 0.8491    |
| 0.2406        | 16.0    | 4700  | 0.3813          | 0.8323   | 0.8323 | 0.8285 | 0.8362    |
| 0.2263        | 16.9974 | 4993  | 0.3808          | 0.8318   | 0.8318 | 0.8275 | 0.8377    |
| 0.3192        | 17.9983 | 5287  | 0.3625          | 0.8412   | 0.8412 | 0.8372 | 0.8484    |
| 0.2003        | 18.9991 | 5581  | 0.3549          | 0.8438   | 0.8438 | 0.8430 | 0.8462    |
| 0.2431        | 20.0    | 5875  | 0.3620          | 0.8425   | 0.8425 | 0.8408 | 0.8467    |
| 0.2654        | 20.9974 | 6168  | 0.3865          | 0.8340   | 0.8340 | 0.8320 | 0.8338    |
| 0.2989        | 21.9983 | 6462  | 0.3632          | 0.8463   | 0.8463 | 0.8449 | 0.8498    |
| 0.2403        | 22.9991 | 6756  | 0.3824          | 0.8301   | 0.8301 | 0.8267 | 0.8304    |
| 0.2393        | 24.0    | 7050  | 0.3607          | 0.8489   | 0.8489 | 0.8473 | 0.8519    |
| 0.2305        | 24.9974 | 7343  | 0.3758          | 0.8365   | 0.8365 | 0.8350 | 0.8401    |
| 0.2654        | 25.9983 | 7637  | 0.3652          | 0.8421   | 0.8421 | 0.8392 | 0.8415    |
| 0.176         | 26.9991 | 7931  | 0.3929          | 0.8306   | 0.8306 | 0.8289 | 0.8385    |
| 0.1893        | 28.0    | 8225  | 0.3794          | 0.8374   | 0.8374 | 0.8365 | 0.8404    |
| 0.2652        | 28.9974 | 8518  | 0.3995          | 0.8387   | 0.8387 | 0.8372 | 0.8423    |
| 0.2029        | 29.9983 | 8812  | 0.3981          | 0.8433   | 0.8433 | 0.8411 | 0.8430    |
| 0.1799        | 30.9991 | 9106  | 0.3554          | 0.8352   | 0.8352 | 0.8340 | 0.8368    |
| 0.2002        | 32.0    | 9400  | 0.3618          | 0.8310   | 0.8310 | 0.8300 | 0.8322    |
| 0.1525        | 32.9974 | 9693  | 0.3629          | 0.8348   | 0.8348 | 0.8343 | 0.8381    |
| 0.1663        | 33.9983 | 9987  | 0.3664          | 0.8425   | 0.8425 | 0.8410 | 0.8427    |
| 0.1728        | 34.9991 | 10281 | 0.3928          | 0.8429   | 0.8429 | 0.8415 | 0.8468    |
| 0.2252        | 36.0    | 10575 | 0.3842          | 0.8421   | 0.8421 | 0.8420 | 0.8443    |
| 0.1554        | 36.9974 | 10868 | 0.3889          | 0.8301   | 0.8301 | 0.8294 | 0.8349    |
| 0.2179        | 37.9983 | 11162 | 0.3775          | 0.8399   | 0.8399 | 0.8389 | 0.8429    |
| 0.1771        | 38.9991 | 11456 | 0.3906          | 0.8306   | 0.8306 | 0.8291 | 0.8324    |
| 0.2167        | 40.0    | 11750 | 0.3870          | 0.8404   | 0.8404 | 0.8382 | 0.8456    |
| 0.1563        | 40.9974 | 12043 | 0.3779          | 0.8284   | 0.8284 | 0.8277 | 0.8288    |
| 0.1419        | 41.9983 | 12337 | 0.4049          | 0.8340   | 0.8340 | 0.8327 | 0.8360    |
| 0.2083        | 42.9991 | 12631 | 0.3800          | 0.8421   | 0.8421 | 0.8410 | 0.8427    |
| 0.2185        | 44.0    | 12925 | 0.3964          | 0.8433   | 0.8433 | 0.8422 | 0.8441    |
| 0.1989        | 44.9974 | 13218 | 0.3870          | 0.8340   | 0.8340 | 0.8339 | 0.8357    |
| 0.1731        | 45.9983 | 13512 | 0.4206          | 0.8340   | 0.8340 | 0.8335 | 0.8357    |
| 0.1831        | 46.9991 | 13806 | 0.4027          | 0.8429   | 0.8429 | 0.8422 | 0.8439    |
| 0.1471        | 48.0    | 14100 | 0.4016          | 0.8318   | 0.8318 | 0.8307 | 0.8320    |
| 0.1879        | 48.9974 | 14393 | 0.3877          | 0.8438   | 0.8438 | 0.8441 | 0.8468    |
| 0.1775        | 49.8723 | 14650 | 0.3984          | 0.8421   | 0.8421 | 0.8408 | 0.8428    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0a0+81ea7a4
- Datasets 2.18.0
- Tokenizers 0.19.1

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