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# smids_1x_beit_base_rms_0001_fold5

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6915
- Accuracy: 0.72

## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2962        | 1.0   | 75   | 0.9009          | 0.4967   |
| 0.8616        | 2.0   | 150  | 0.8829          | 0.5333   |
| 0.8905        | 3.0   | 225  | 0.8472          | 0.5367   |
| 0.8302        | 4.0   | 300  | 0.9953          | 0.5067   |
| 0.8678        | 5.0   | 375  | 0.8690          | 0.525    |
| 0.8529        | 6.0   | 450  | 0.8769          | 0.5283   |
| 0.8841        | 7.0   | 525  | 0.8786          | 0.53     |
| 0.8327        | 8.0   | 600  | 0.8584          | 0.5367   |
| 0.8106        | 9.0   | 675  | 0.8478          | 0.5817   |
| 0.8163        | 10.0  | 750  | 0.8420          | 0.54     |
| 0.8203        | 11.0  | 825  | 0.8233          | 0.615    |
| 0.849         | 12.0  | 900  | 0.8207          | 0.56     |
| 0.7448        | 13.0  | 975  | 0.9969          | 0.48     |
| 0.8104        | 14.0  | 1050 | 0.8107          | 0.5717   |
| 0.8455        | 15.0  | 1125 | 0.8387          | 0.56     |
| 0.7497        | 16.0  | 1200 | 0.7795          | 0.5983   |
| 0.7595        | 17.0  | 1275 | 0.7579          | 0.63     |
| 0.7118        | 18.0  | 1350 | 0.7723          | 0.63     |
| 0.7898        | 19.0  | 1425 | 0.7567          | 0.635    |
| 0.7627        | 20.0  | 1500 | 0.7797          | 0.6367   |
| 0.8345        | 21.0  | 1575 | 0.7467          | 0.6217   |
| 0.745         | 22.0  | 1650 | 0.7264          | 0.655    |
| 0.7402        | 23.0  | 1725 | 0.7241          | 0.6633   |
| 0.6239        | 24.0  | 1800 | 0.7183          | 0.665    |
| 0.6855        | 25.0  | 1875 | 0.7858          | 0.6333   |
| 0.7229        | 26.0  | 1950 | 0.7404          | 0.6333   |
| 0.7229        | 27.0  | 2025 | 0.7258          | 0.68     |
| 0.7197        | 28.0  | 2100 | 0.6990          | 0.6917   |
| 0.7057        | 29.0  | 2175 | 0.7035          | 0.68     |
| 0.7315        | 30.0  | 2250 | 0.7188          | 0.6683   |
| 0.6562        | 31.0  | 2325 | 0.7484          | 0.6283   |
| 0.6918        | 32.0  | 2400 | 0.6817          | 0.6917   |
| 0.6871        | 33.0  | 2475 | 0.7362          | 0.6717   |
| 0.6724        | 34.0  | 2550 | 0.6752          | 0.7      |
| 0.6677        | 35.0  | 2625 | 0.6742          | 0.6933   |
| 0.6138        | 36.0  | 2700 | 0.6850          | 0.6867   |
| 0.582         | 37.0  | 2775 | 0.6804          | 0.6817   |
| 0.6731        | 38.0  | 2850 | 0.6827          | 0.6917   |
| 0.5577        | 39.0  | 2925 | 0.7025          | 0.6833   |
| 0.5702        | 40.0  | 3000 | 0.6473          | 0.7117   |
| 0.578         | 41.0  | 3075 | 0.6455          | 0.72     |
| 0.6074        | 42.0  | 3150 | 0.6478          | 0.715    |
| 0.6019        | 43.0  | 3225 | 0.6442          | 0.7      |
| 0.5836        | 44.0  | 3300 | 0.6632          | 0.6983   |
| 0.5466        | 45.0  | 3375 | 0.6605          | 0.68     |
| 0.4891        | 46.0  | 3450 | 0.6690          | 0.715    |
| 0.5107        | 47.0  | 3525 | 0.6729          | 0.7167   |
| 0.3981        | 48.0  | 3600 | 0.7111          | 0.7017   |
| 0.434         | 49.0  | 3675 | 0.6850          | 0.7183   |
| 0.3741        | 50.0  | 3750 | 0.6915          | 0.72     |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

abnormal_sperm non-sperm normal_sperm