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# cvt-21-finetuned-brs2

This model is a fine-tuned version of [microsoft/cvt-21](https://huggingface.co/microsoft/cvt-21) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6947
- Accuracy: 0.6604
- F1: 0.6087
- Precision (ppv): 0.5385
- Recall (sensitivity): 0.7
- Specificity: 0.6364
- Npv: 0.7778
- Auc: 0.6682

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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 | Accuracy | F1     | Precision (ppv) | Recall (sensitivity) | Specificity | Npv    | Auc    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
| 0.8177        | 1.89  | 100  | 0.7113          | 0.5283   | 0.5098 | 0.4194          | 0.65                 | 0.4545      | 0.6818 | 0.5523 |
| 0.736         | 3.77  | 200  | 0.7178          | 0.5283   | 0.3902 | 0.3810          | 0.4                  | 0.6061      | 0.625  | 0.5030 |
| 0.5978        | 5.66  | 300  | 0.6889          | 0.6038   | 0.5532 | 0.4815          | 0.65                 | 0.5758      | 0.7308 | 0.6129 |
| 0.5576        | 7.55  | 400  | 0.7349          | 0.4717   | 0.5484 | 0.4048          | 0.85                 | 0.2424      | 0.7273 | 0.5462 |
| 0.5219        | 9.43  | 500  | 0.6522          | 0.6038   | 0.4    | 0.4667          | 0.35                 | 0.7576      | 0.6579 | 0.5538 |
| 0.5326        | 11.32 | 600  | 0.6665          | 0.6226   | 0.5238 | 0.5             | 0.55                 | 0.6667      | 0.7097 | 0.6083 |
| 0.4381        | 13.21 | 700  | 0.7685          | 0.4717   | 0.5333 | 0.4             | 0.8                  | 0.2727      | 0.6923 | 0.5364 |
| 0.5598        | 15.09 | 800  | 0.7212          | 0.5283   | 0.1935 | 0.2727          | 0.15                 | 0.7576      | 0.5952 | 0.4538 |
| 0.6887        | 16.98 | 900  | 0.6985          | 0.6604   | 0.64   | 0.5333          | 0.8                  | 0.5758      | 0.8261 | 0.6879 |
| 0.7594        | 18.87 | 1000 | 0.7040          | 0.5472   | 0.4286 | 0.4091          | 0.45                 | 0.6061      | 0.6452 | 0.5280 |
| 0.2177        | 20.75 | 1100 | 0.8056          | 0.4528   | 0.5397 | 0.3953          | 0.85                 | 0.2121      | 0.7    | 0.5311 |
| 0.4893        | 22.64 | 1200 | 0.8821          | 0.3396   | 0.3860 | 0.2973          | 0.55                 | 0.2121      | 0.4375 | 0.3811 |
| 0.5994        | 24.53 | 1300 | 0.8059          | 0.5660   | 0.5660 | 0.4545          | 0.75                 | 0.4545      | 0.75   | 0.6023 |
| 0.5179        | 26.42 | 1400 | 0.6750          | 0.6038   | 0.4615 | 0.4737          | 0.45                 | 0.6970      | 0.6765 | 0.5735 |
| 0.198         | 28.3  | 1500 | 0.7448          | 0.3962   | 0.3333 | 0.2857          | 0.4                  | 0.3939      | 0.52   | 0.3970 |
| 0.6536        | 30.19 | 1600 | 0.7555          | 0.5094   | 0.4583 | 0.3929          | 0.55                 | 0.4848      | 0.64   | 0.5174 |
| 0.7558        | 32.08 | 1700 | 0.6664          | 0.5849   | 0.4762 | 0.4545          | 0.5                  | 0.6364      | 0.6774 | 0.5682 |
| 0.4915        | 33.96 | 1800 | 0.9213          | 0.3962   | 0.5152 | 0.3696          | 0.85                 | 0.1212      | 0.5714 | 0.4856 |
| 0.3661        | 35.85 | 1900 | 0.9202          | 0.4528   | 0.4912 | 0.3784          | 0.7                  | 0.3030      | 0.625  | 0.5015 |
| 0.4838        | 37.74 | 2000 | 0.9297          | 0.4528   | 0.5085 | 0.3846          | 0.75                 | 0.2727      | 0.6429 | 0.5114 |
| 0.8461        | 39.62 | 2100 | 0.9464          | 0.4717   | 0.5758 | 0.4130          | 0.95                 | 0.1818      | 0.8571 | 0.5659 |
| 0.6937        | 41.51 | 2200 | 0.7129          | 0.5094   | 0.48   | 0.4             | 0.6                  | 0.4545      | 0.6522 | 0.5273 |
| 0.6302        | 43.4  | 2300 | 0.6866          | 0.5849   | 0.6071 | 0.4722          | 0.85                 | 0.4242      | 0.8235 | 0.6371 |
| 0.0793        | 45.28 | 2400 | 0.7791          | 0.5094   | 0.5517 | 0.4211          | 0.8                  | 0.3333      | 0.7333 | 0.5667 |
| 0.464         | 47.17 | 2500 | 0.8116          | 0.4340   | 0.4444 | 0.3529          | 0.6                  | 0.3333      | 0.5789 | 0.4667 |
| 0.6131        | 49.06 | 2600 | 0.5970          | 0.6226   | 0.5455 | 0.5             | 0.6                  | 0.6364      | 0.7241 | 0.6182 |
| 0.6937        | 50.94 | 2700 | 0.8201          | 0.4340   | 0.4    | 0.3333          | 0.5                  | 0.3939      | 0.5652 | 0.4470 |
| 0.6552        | 52.83 | 2800 | 0.7168          | 0.5660   | 0.5306 | 0.4483          | 0.65                 | 0.5152      | 0.7083 | 0.5826 |
| 0.7749        | 54.72 | 2900 | 0.6875          | 0.5849   | 0.5217 | 0.4615          | 0.6                  | 0.5758      | 0.7037 | 0.5879 |
| 0.9482        | 56.6  | 3000 | 0.6392          | 0.6226   | 0.6296 | 0.5             | 0.85                 | 0.4848      | 0.8421 | 0.6674 |
| 0.2467        | 58.49 | 3100 | 0.6281          | 0.6038   | 0.5333 | 0.48            | 0.6                  | 0.6061      | 0.7143 | 0.6030 |
| 0.2903        | 60.38 | 3200 | 0.7383          | 0.5472   | 0.5556 | 0.4412          | 0.75                 | 0.4242      | 0.7368 | 0.5871 |
| 0.5859        | 62.26 | 3300 | 0.7191          | 0.6226   | 0.5652 | 0.5             | 0.65                 | 0.6061      | 0.7407 | 0.6280 |
| 0.3815        | 64.15 | 3400 | 0.7469          | 0.5283   | 0.4444 | 0.4             | 0.5                  | 0.5455      | 0.6429 | 0.5227 |
| 0.531         | 66.04 | 3500 | 0.7566          | 0.6226   | 0.5652 | 0.5             | 0.65                 | 0.6061      | 0.7407 | 0.6280 |
| 0.3892        | 67.92 | 3600 | 0.8168          | 0.5660   | 0.5490 | 0.4516          | 0.7                  | 0.4848      | 0.7273 | 0.5924 |
| 0.6487        | 69.81 | 3700 | 0.9077          | 0.4340   | 0.4643 | 0.3611          | 0.65                 | 0.3030      | 0.5882 | 0.4765 |
| 0.5525        | 71.7  | 3800 | 0.6961          | 0.6038   | 0.5116 | 0.4783          | 0.55                 | 0.6364      | 0.7    | 0.5932 |
| 0.3137        | 73.58 | 3900 | 1.0817          | 0.3774   | 0.4590 | 0.3415          | 0.7                  | 0.1818      | 0.5    | 0.4409 |
| 0.3526        | 75.47 | 4000 | 0.7684          | 0.5472   | 0.5862 | 0.4474          | 0.85                 | 0.3636      | 0.8    | 0.6068 |
| 0.5938        | 77.36 | 4100 | 0.8786          | 0.4340   | 0.4828 | 0.3684          | 0.7                  | 0.2727      | 0.6    | 0.4864 |
| 0.2431        | 79.25 | 4200 | 0.8925          | 0.4151   | 0.4746 | 0.3590          | 0.7                  | 0.2424      | 0.5714 | 0.4712 |
| 0.1021        | 81.13 | 4300 | 1.0740          | 0.4528   | 0.4727 | 0.3714          | 0.65                 | 0.3333      | 0.6111 | 0.4917 |
| 0.3429        | 83.02 | 4400 | 0.7723          | 0.4906   | 0.5091 | 0.4             | 0.7                  | 0.3636      | 0.6667 | 0.5318 |
| 0.3836        | 84.91 | 4500 | 0.7247          | 0.5472   | 0.5556 | 0.4412          | 0.75                 | 0.4242      | 0.7368 | 0.5871 |
| 0.4099        | 86.79 | 4600 | 0.8508          | 0.4340   | 0.4828 | 0.3684          | 0.7                  | 0.2727      | 0.6    | 0.4864 |
| 0.8264        | 88.68 | 4700 | 0.7682          | 0.5849   | 0.5769 | 0.4688          | 0.75                 | 0.4848      | 0.7619 | 0.6174 |
| 0.1928        | 90.57 | 4800 | 0.8738          | 0.4906   | 0.5574 | 0.4146          | 0.85                 | 0.2727      | 0.75   | 0.5614 |
| 0.3422        | 92.45 | 4900 | 0.8810          | 0.5660   | 0.5965 | 0.4595          | 0.85                 | 0.3939      | 0.8125 | 0.6220 |
| 0.5524        | 94.34 | 5000 | 1.0801          | 0.3774   | 0.4923 | 0.3556          | 0.8                  | 0.1212      | 0.5    | 0.4606 |
| 0.464         | 96.23 | 5100 | 0.9417          | 0.5283   | 0.5902 | 0.4390          | 0.9                  | 0.3030      | 0.8333 | 0.6015 |
| 0.7182        | 98.11 | 5200 | 1.0335          | 0.4151   | 0.4746 | 0.3590          | 0.7                  | 0.2424      | 0.5714 | 0.4712 |
| 0.604         | 100.0 | 5300 | 0.6947          | 0.6604   | 0.6087 | 0.5385          | 0.7                  | 0.6364      | 0.7778 | 0.6682 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1

event no_event