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# resnet-18-feature-extraction

This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1485
- Accuracy: 0.95
- Precision: 0.9653
- Recall: 0.9789
- F1: 0.9720
- Roc Auc: 0.8505

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 | Precision | Recall | F1     | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log        | 0.8   | 2    | 0.6232          | 0.75     | 0.9636    | 0.7465 | 0.8413 | 0.7621  |
| No log        | 1.8   | 4    | 0.6971          | 0.4875   | 1.0       | 0.4225 | 0.5941 | 0.7113  |
| No log        | 2.8   | 6    | 0.7915          | 0.2875   | 1.0       | 0.1972 | 0.3294 | 0.5986  |
| No log        | 3.8   | 8    | 0.8480          | 0.2875   | 1.0       | 0.1972 | 0.3294 | 0.5986  |
| 0.8651        | 4.8   | 10   | 0.9094          | 0.2562   | 1.0       | 0.1620 | 0.2788 | 0.5810  |
| 0.8651        | 5.8   | 12   | 0.7470          | 0.5625   | 1.0       | 0.5070 | 0.6729 | 0.7535  |
| 0.8651        | 6.8   | 14   | 0.5915          | 0.85     | 1.0       | 0.8310 | 0.9077 | 0.9155  |
| 0.8651        | 7.8   | 16   | 0.4817          | 0.8875   | 0.9844    | 0.8873 | 0.9333 | 0.8881  |
| 0.8651        | 8.8   | 18   | 0.3455          | 0.9187   | 0.9778    | 0.9296 | 0.9531 | 0.8815  |
| 0.5349        | 9.8   | 20   | 0.2966          | 0.9187   | 0.9708    | 0.9366 | 0.9534 | 0.8572  |
| 0.5349        | 10.8  | 22   | 0.2347          | 0.95     | 0.9653    | 0.9789 | 0.9720 | 0.8505  |
| 0.5349        | 11.8  | 24   | 0.2468          | 0.9313   | 0.9645    | 0.9577 | 0.9611 | 0.8400  |
| 0.5349        | 12.8  | 26   | 0.2310          | 0.9563   | 0.9720    | 0.9789 | 0.9754 | 0.8783  |
| 0.5349        | 13.8  | 28   | 0.2083          | 0.9313   | 0.9580    | 0.9648 | 0.9614 | 0.8157  |
| 0.3593        | 14.8  | 30   | 0.1840          | 0.9375   | 0.9521    | 0.9789 | 0.9653 | 0.7950  |
| 0.3593        | 15.8  | 32   | 0.1947          | 0.9375   | 0.9648    | 0.9648 | 0.9648 | 0.8435  |
| 0.3593        | 16.8  | 34   | 0.1837          | 0.9313   | 0.9517    | 0.9718 | 0.9617 | 0.7915  |
| 0.3593        | 17.8  | 36   | 0.1819          | 0.9437   | 0.9524    | 0.9859 | 0.9689 | 0.7985  |
| 0.3593        | 18.8  | 38   | 0.1924          | 0.9437   | 0.9650    | 0.9718 | 0.9684 | 0.8470  |
| 0.2737        | 19.8  | 40   | 0.1990          | 0.95     | 0.9653    | 0.9789 | 0.9720 | 0.8505  |
| 0.2737        | 20.8  | 42   | 0.1759          | 0.95     | 0.9718    | 0.9718 | 0.9718 | 0.8748  |
| 0.2737        | 21.8  | 44   | 0.1804          | 0.9313   | 0.9517    | 0.9718 | 0.9617 | 0.7915  |
| 0.2737        | 22.8  | 46   | 0.1666          | 0.9313   | 0.9517    | 0.9718 | 0.9617 | 0.7915  |
| 0.2737        | 23.8  | 48   | 0.1534          | 0.9437   | 0.9524    | 0.9859 | 0.9689 | 0.7985  |
| 0.2278        | 24.8  | 50   | 0.1612          | 0.9375   | 0.9521    | 0.9789 | 0.9653 | 0.7950  |
| 0.2278        | 25.8  | 52   | 0.1535          | 0.9437   | 0.9586    | 0.9789 | 0.9686 | 0.8228  |
| 0.2278        | 26.8  | 54   | 0.1568          | 0.9437   | 0.9716    | 0.9648 | 0.9682 | 0.8713  |
| 0.2278        | 27.8  | 56   | 0.2107          | 0.9375   | 0.9714    | 0.9577 | 0.9645 | 0.8678  |
| 0.2278        | 28.8  | 58   | 0.1592          | 0.9313   | 0.9517    | 0.9718 | 0.9617 | 0.7915  |
| 0.2057        | 29.8  | 60   | 0.1557          | 0.9375   | 0.9648    | 0.9648 | 0.9648 | 0.8435  |
| 0.2057        | 30.8  | 62   | 0.1714          | 0.9437   | 0.9650    | 0.9718 | 0.9684 | 0.8470  |
| 0.2057        | 31.8  | 64   | 0.1571          | 0.95     | 0.9653    | 0.9789 | 0.9720 | 0.8505  |
| 0.2057        | 32.8  | 66   | 0.1574          | 0.9375   | 0.9583    | 0.9718 | 0.9650 | 0.8192  |
| 0.2057        | 33.8  | 68   | 0.1423          | 0.9563   | 0.9720    | 0.9789 | 0.9754 | 0.8783  |
| 0.2           | 34.8  | 70   | 0.1677          | 0.9437   | 0.9650    | 0.9718 | 0.9684 | 0.8470  |
| 0.2           | 35.8  | 72   | 0.1560          | 0.9375   | 0.9583    | 0.9718 | 0.9650 | 0.8192  |
| 0.2           | 36.8  | 74   | 0.1594          | 0.9375   | 0.9521    | 0.9789 | 0.9653 | 0.7950  |
| 0.2           | 37.8  | 76   | 0.1512          | 0.9437   | 0.9586    | 0.9789 | 0.9686 | 0.8228  |
| 0.2           | 38.8  | 78   | 0.1396          | 0.9563   | 0.9655    | 0.9859 | 0.9756 | 0.8541  |
| 0.1838        | 39.8  | 80   | 0.1509          | 0.9375   | 0.9583    | 0.9718 | 0.9650 | 0.8192  |
| 0.1838        | 40.8  | 82   | 0.1529          | 0.95     | 0.9718    | 0.9718 | 0.9718 | 0.8748  |
| 0.1838        | 41.8  | 84   | 0.1506          | 0.95     | 0.9653    | 0.9789 | 0.9720 | 0.8505  |
| 0.1838        | 42.8  | 86   | 0.1549          | 0.95     | 0.9653    | 0.9789 | 0.9720 | 0.8505  |
| 0.1838        | 43.8  | 88   | 0.1331          | 0.9563   | 0.9655    | 0.9859 | 0.9756 | 0.8541  |
| 0.1872        | 44.8  | 90   | 0.1409          | 0.9437   | 0.9524    | 0.9859 | 0.9689 | 0.7985  |
| 0.1872        | 45.8  | 92   | 0.1639          | 0.9375   | 0.9583    | 0.9718 | 0.9650 | 0.8192  |
| 0.1872        | 46.8  | 94   | 0.1391          | 0.95     | 0.9589    | 0.9859 | 0.9722 | 0.8263  |
| 0.1872        | 47.8  | 96   | 0.1436          | 0.9563   | 0.9655    | 0.9859 | 0.9756 | 0.8541  |
| 0.1872        | 48.8  | 98   | 0.1442          | 0.9437   | 0.9586    | 0.9789 | 0.9686 | 0.8228  |
| 0.185         | 49.8  | 100  | 0.1485          | 0.95     | 0.9653    | 0.9789 | 0.9720 | 0.8505  |


### Framework versions

- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1

glaucoma normal