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# 14-classifier-finetuned-padchest

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9826
- F1: 0.6620

## 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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.0848        | 1.0   | 18   | 2.0904          | 0.0451 |
| 2.0822        | 2.0   | 36   | 2.0842          | 0.0848 |
| 2.0681        | 3.0   | 54   | 2.0742          | 0.1092 |
| 2.0568        | 4.0   | 72   | 2.0624          | 0.1443 |
| 2.0363        | 5.0   | 90   | 2.0420          | 0.1747 |
| 2.0235        | 6.0   | 108  | 2.0288          | 0.1533 |
| 1.9984        | 7.0   | 126  | 2.0008          | 0.1816 |
| 1.9647        | 8.0   | 144  | 1.9752          | 0.1849 |
| 1.9257        | 9.0   | 162  | 1.9481          | 0.2075 |
| 1.8791        | 10.0  | 180  | 1.9060          | 0.2219 |
| 1.8561        | 11.0  | 198  | 1.8678          | 0.2734 |
| 1.803         | 12.0  | 216  | 1.8322          | 0.2039 |
| 1.7461        | 13.0  | 234  | 1.8045          | 0.2939 |
| 1.7169        | 14.0  | 252  | 1.8216          | 0.3012 |
| 1.6773        | 15.0  | 270  | 1.7588          | 0.3166 |
| 1.6724        | 16.0  | 288  | 1.7379          | 0.2726 |
| 1.6286        | 17.0  | 306  | 1.7109          | 0.3248 |
| 1.5533        | 18.0  | 324  | 1.6492          | 0.3294 |
| 1.5075        | 19.0  | 342  | 1.5951          | 0.3394 |
| 1.4789        | 20.0  | 360  | 1.5657          | 0.3643 |
| 1.4077        | 21.0  | 378  | 1.5287          | 0.3665 |
| 1.4146        | 22.0  | 396  | 1.4897          | 0.4099 |
| 1.3583        | 23.0  | 414  | 1.4704          | 0.3765 |
| 1.3486        | 24.0  | 432  | 1.4469          | 0.3813 |
| 1.2947        | 25.0  | 450  | 1.4228          | 0.4049 |
| 1.3272        | 26.0  | 468  | 1.4035          | 0.4203 |
| 1.3048        | 27.0  | 486  | 1.3907          | 0.4316 |
| 1.2898        | 28.0  | 504  | 1.3992          | 0.4520 |
| 1.2204        | 29.0  | 522  | 1.3751          | 0.4952 |
| 1.2298        | 30.0  | 540  | 1.3658          | 0.4771 |
| 1.2036        | 31.0  | 558  | 1.3464          | 0.4723 |
| 1.2314        | 32.0  | 576  | 1.3276          | 0.5061 |
| 1.2201        | 33.0  | 594  | 1.3068          | 0.5027 |
| 1.1737        | 34.0  | 612  | 1.2978          | 0.5161 |
| 1.2102        | 35.0  | 630  | 1.2962          | 0.4961 |
| 1.156         | 36.0  | 648  | 1.2793          | 0.5172 |
| 1.1707        | 37.0  | 666  | 1.2715          | 0.5125 |
| 1.149         | 38.0  | 684  | 1.2728          | 0.4986 |
| 1.1685        | 39.0  | 702  | 1.2525          | 0.5101 |
| 1.1212        | 40.0  | 720  | 1.2446          | 0.5100 |
| 1.095         | 41.0  | 738  | 1.2365          | 0.5119 |
| 1.1166        | 42.0  | 756  | 1.2241          | 0.5294 |
| 1.0775        | 43.0  | 774  | 1.2175          | 0.5234 |
| 1.0768        | 44.0  | 792  | 1.2041          | 0.5165 |
| 1.0395        | 45.0  | 810  | 1.1995          | 0.5284 |
| 1.0857        | 46.0  | 828  | 1.2031          | 0.5316 |
| 1.0447        | 47.0  | 846  | 1.1954          | 0.5096 |
| 1.0504        | 48.0  | 864  | 1.1708          | 0.5349 |
| 1.0229        | 49.0  | 882  | 1.1656          | 0.5468 |
| 1.0715        | 50.0  | 900  | 1.1625          | 0.5505 |
| 1.0401        | 51.0  | 918  | 1.1619          | 0.5458 |
| 1.0477        | 52.0  | 936  | 1.1373          | 0.5608 |
| 1.009         | 53.0  | 954  | 1.1425          | 0.5740 |
| 1.0078        | 54.0  | 972  | 1.1397          | 0.5622 |
| 0.9709        | 55.0  | 990  | 1.1503          | 0.5813 |
| 0.9989        | 56.0  | 1008 | 1.1271          | 0.5761 |
| 0.9704        | 57.0  | 1026 | 1.1332          | 0.5691 |
| 0.9537        | 58.0  | 1044 | 1.1113          | 0.5910 |
| 0.9722        | 59.0  | 1062 | 1.1047          | 0.5832 |
| 0.9889        | 60.0  | 1080 | 1.1005          | 0.5815 |
| 0.9682        | 61.0  | 1098 | 1.0862          | 0.6137 |
| 0.9609        | 62.0  | 1116 | 1.0737          | 0.6148 |
| 0.9688        | 63.0  | 1134 | 1.0580          | 0.6238 |
| 0.9488        | 64.0  | 1152 | 1.0645          | 0.6253 |
| 0.926         | 65.0  | 1170 | 1.0576          | 0.6188 |
| 0.9689        | 66.0  | 1188 | 1.0438          | 0.6210 |
| 0.9445        | 67.0  | 1206 | 1.0409          | 0.6319 |
| 0.938         | 68.0  | 1224 | 1.0302          | 0.6397 |
| 0.9134        | 69.0  | 1242 | 1.0346          | 0.6337 |
| 0.9125        | 70.0  | 1260 | 1.0221          | 0.6575 |
| 0.8879        | 71.0  | 1278 | 1.0146          | 0.6633 |
| 0.9212        | 72.0  | 1296 | 1.0206          | 0.6384 |
| 0.9259        | 73.0  | 1314 | 1.0255          | 0.6213 |
| 0.9224        | 74.0  | 1332 | 1.0190          | 0.6417 |
| 0.9249        | 75.0  | 1350 | 1.0063          | 0.6371 |
| 0.8888        | 76.0  | 1368 | 0.9951          | 0.6458 |
| 0.8799        | 77.0  | 1386 | 1.0045          | 0.6436 |
| 0.9186        | 78.0  | 1404 | 0.9871          | 0.6449 |
| 0.9087        | 79.0  | 1422 | 1.0031          | 0.6611 |
| 0.914         | 80.0  | 1440 | 0.9893          | 0.6501 |
| 0.9012        | 81.0  | 1458 | 0.9876          | 0.6441 |
| 0.8748        | 82.0  | 1476 | 0.9873          | 0.6533 |
| 0.8736        | 83.0  | 1494 | 0.9951          | 0.6524 |
| 0.892         | 84.0  | 1512 | 1.0012          | 0.6563 |
| 0.8746        | 85.0  | 1530 | 0.9944          | 0.6684 |
| 0.8769        | 86.0  | 1548 | 0.9841          | 0.6558 |
| 0.8816        | 87.0  | 1566 | 0.9930          | 0.6551 |
| 0.8889        | 88.0  | 1584 | 0.9880          | 0.6497 |
| 0.8705        | 89.0  | 1602 | 0.9874          | 0.6564 |
| 0.8607        | 90.0  | 1620 | 0.9850          | 0.6471 |
| 0.86          | 91.0  | 1638 | 0.9851          | 0.6572 |
| 0.878         | 92.0  | 1656 | 0.9835          | 0.6553 |
| 0.8592        | 93.0  | 1674 | 0.9784          | 0.6577 |
| 0.8699        | 94.0  | 1692 | 0.9783          | 0.6568 |
| 0.8413        | 95.0  | 1710 | 0.9909          | 0.6519 |
| 0.8944        | 96.0  | 1728 | 0.9759          | 0.6581 |
| 0.8404        | 97.0  | 1746 | 0.9834          | 0.6640 |
| 0.8954        | 98.0  | 1764 | 0.9785          | 0.6582 |
| 0.8539        | 99.0  | 1782 | 0.9746          | 0.6528 |
| 0.8732        | 100.0 | 1800 | 0.9826          | 0.6620 |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.19.0
- Tokenizers 0.12.1

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