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# smids_1x_deit_small_rms_0001_fold2

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6765
- Accuracy: 0.7571

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1018        | 1.0   | 75   | 1.0371          | 0.4260   |
| 0.991         | 2.0   | 150  | 0.9921          | 0.4792   |
| 0.9572        | 3.0   | 225  | 0.9534          | 0.4692   |
| 0.9605        | 4.0   | 300  | 0.9410          | 0.4942   |
| 1.0177        | 5.0   | 375  | 0.9782          | 0.4792   |
| 0.8824        | 6.0   | 450  | 0.9530          | 0.4775   |
| 0.9937        | 7.0   | 525  | 1.2068          | 0.4143   |
| 0.9218        | 8.0   | 600  | 0.9562          | 0.4842   |
| 0.9543        | 9.0   | 675  | 0.9220          | 0.4892   |
| 0.9236        | 10.0  | 750  | 0.9222          | 0.4958   |
| 0.9252        | 11.0  | 825  | 0.8952          | 0.5075   |
| 0.8897        | 12.0  | 900  | 0.8977          | 0.5042   |
| 0.8737        | 13.0  | 975  | 0.8116          | 0.5691   |
| 0.8039        | 14.0  | 1050 | 0.7757          | 0.5790   |
| 0.7793        | 15.0  | 1125 | 0.8219          | 0.5824   |
| 0.8231        | 16.0  | 1200 | 0.7679          | 0.6057   |
| 0.8017        | 17.0  | 1275 | 0.7881          | 0.5874   |
| 0.7891        | 18.0  | 1350 | 0.8079          | 0.5990   |
| 0.7545        | 19.0  | 1425 | 0.7312          | 0.6456   |
| 0.7578        | 20.0  | 1500 | 0.7753          | 0.6123   |
| 0.8565        | 21.0  | 1575 | 0.7816          | 0.6073   |
| 0.7262        | 22.0  | 1650 | 0.8273          | 0.5840   |
| 0.7951        | 23.0  | 1725 | 0.7247          | 0.6339   |
| 0.7867        | 24.0  | 1800 | 0.7753          | 0.6173   |
| 0.7108        | 25.0  | 1875 | 0.7213          | 0.6805   |
| 0.6679        | 26.0  | 1950 | 0.7131          | 0.6556   |
| 0.7183        | 27.0  | 2025 | 0.7432          | 0.6456   |
| 0.6589        | 28.0  | 2100 | 0.6919          | 0.6938   |
| 0.6988        | 29.0  | 2175 | 0.7014          | 0.6689   |
| 0.6704        | 30.0  | 2250 | 0.6664          | 0.7038   |
| 0.6348        | 31.0  | 2325 | 0.6647          | 0.7038   |
| 0.6316        | 32.0  | 2400 | 0.6641          | 0.6988   |
| 0.5915        | 33.0  | 2475 | 0.6743          | 0.6839   |
| 0.6102        | 34.0  | 2550 | 0.6568          | 0.7038   |
| 0.5452        | 35.0  | 2625 | 0.6346          | 0.7271   |
| 0.5721        | 36.0  | 2700 | 0.6475          | 0.7255   |
| 0.5908        | 37.0  | 2775 | 0.6240          | 0.7388   |
| 0.6069        | 38.0  | 2850 | 0.6538          | 0.7354   |
| 0.4947        | 39.0  | 2925 | 0.6146          | 0.7438   |
| 0.4469        | 40.0  | 3000 | 0.6694          | 0.7038   |
| 0.5595        | 41.0  | 3075 | 0.5969          | 0.7438   |
| 0.524         | 42.0  | 3150 | 0.6251          | 0.7438   |
| 0.5223        | 43.0  | 3225 | 0.6144          | 0.7338   |
| 0.4414        | 44.0  | 3300 | 0.6374          | 0.7404   |
| 0.5093        | 45.0  | 3375 | 0.6328          | 0.7488   |
| 0.4116        | 46.0  | 3450 | 0.6556          | 0.7537   |
| 0.414         | 47.0  | 3525 | 0.6472          | 0.7604   |
| 0.445         | 48.0  | 3600 | 0.6566          | 0.7488   |
| 0.3661        | 49.0  | 3675 | 0.6775          | 0.7504   |
| 0.3935        | 50.0  | 3750 | 0.6765          | 0.7571   |


### 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