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# smids_5x_beit_base_rms_0001_fold2

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: 0.9869
- Accuracy: 0.9068

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3386        | 1.0   | 375   | 0.2631          | 0.8902   |
| 0.2769        | 2.0   | 750   | 0.2812          | 0.8852   |
| 0.1948        | 3.0   | 1125  | 0.4161          | 0.8686   |
| 0.1489        | 4.0   | 1500  | 0.3316          | 0.8852   |
| 0.1015        | 5.0   | 1875  | 0.3966          | 0.8835   |
| 0.0659        | 6.0   | 2250  | 0.5521          | 0.8686   |
| 0.0987        | 7.0   | 2625  | 0.4706          | 0.8852   |
| 0.0304        | 8.0   | 3000  | 0.6100          | 0.8835   |
| 0.0177        | 9.0   | 3375  | 0.5599          | 0.8835   |
| 0.0365        | 10.0  | 3750  | 0.5970          | 0.8902   |
| 0.07          | 11.0  | 4125  | 0.5587          | 0.8869   |
| 0.025         | 12.0  | 4500  | 0.6283          | 0.8885   |
| 0.013         | 13.0  | 4875  | 0.4540          | 0.9035   |
| 0.0155        | 14.0  | 5250  | 0.6593          | 0.8869   |
| 0.0612        | 15.0  | 5625  | 0.6571          | 0.8935   |
| 0.0058        | 16.0  | 6000  | 0.6333          | 0.8835   |
| 0.0564        | 17.0  | 6375  | 0.5490          | 0.8918   |
| 0.0204        | 18.0  | 6750  | 0.7225          | 0.8985   |
| 0.0128        | 19.0  | 7125  | 0.4844          | 0.9135   |
| 0.0241        | 20.0  | 7500  | 0.5085          | 0.9018   |
| 0.0042        | 21.0  | 7875  | 0.5500          | 0.9135   |
| 0.0209        | 22.0  | 8250  | 0.6987          | 0.8869   |
| 0.0277        | 23.0  | 8625  | 0.7227          | 0.8902   |
| 0.027         | 24.0  | 9000  | 0.8023          | 0.8769   |
| 0.0061        | 25.0  | 9375  | 0.7219          | 0.8985   |
| 0.0004        | 26.0  | 9750  | 0.7303          | 0.8935   |
| 0.0002        | 27.0  | 10125 | 0.6194          | 0.9118   |
| 0.0002        | 28.0  | 10500 | 0.7358          | 0.9085   |
| 0.0068        | 29.0  | 10875 | 0.7598          | 0.9002   |
| 0.0002        | 30.0  | 11250 | 0.7703          | 0.8935   |
| 0.0136        | 31.0  | 11625 | 0.7951          | 0.8902   |
| 0.0053        | 32.0  | 12000 | 0.8891          | 0.8918   |
| 0.0038        | 33.0  | 12375 | 0.7625          | 0.9018   |
| 0.0002        | 34.0  | 12750 | 0.8776          | 0.9052   |
| 0.0           | 35.0  | 13125 | 0.9210          | 0.9002   |
| 0.0195        | 36.0  | 13500 | 0.7510          | 0.9151   |
| 0.0008        | 37.0  | 13875 | 0.7794          | 0.9135   |
| 0.0007        | 38.0  | 14250 | 0.8315          | 0.9085   |
| 0.0005        | 39.0  | 14625 | 0.7854          | 0.9151   |
| 0.0033        | 40.0  | 15000 | 0.8459          | 0.9101   |
| 0.0001        | 41.0  | 15375 | 0.9023          | 0.9002   |
| 0.0027        | 42.0  | 15750 | 1.0108          | 0.9018   |
| 0.0026        | 43.0  | 16125 | 1.0264          | 0.8952   |
| 0.0026        | 44.0  | 16500 | 0.9790          | 0.9035   |
| 0.0027        | 45.0  | 16875 | 0.9445          | 0.9101   |
| 0.0           | 46.0  | 17250 | 0.9135          | 0.9185   |
| 0.0057        | 47.0  | 17625 | 0.9222          | 0.9085   |
| 0.0           | 48.0  | 18000 | 0.9390          | 0.9085   |
| 0.0052        | 49.0  | 18375 | 0.9876          | 0.9052   |
| 0.0025        | 50.0  | 18750 | 0.9869          | 0.9068   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm