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# smids_1x_beit_base_rms_0001_fold4

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: 0.6670
- Accuracy: 0.7333

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1121        | 1.0   | 75   | 1.0797          | 0.495    |
| 1.1167        | 2.0   | 150  | 1.0990          | 0.3383   |
| 1.1124        | 3.0   | 225  | 1.0945          | 0.3583   |
| 1.0914        | 4.0   | 300  | 1.0750          | 0.35     |
| 1.0647        | 5.0   | 375  | 0.8667          | 0.5733   |
| 0.9583        | 6.0   | 450  | 0.8905          | 0.51     |
| 0.8629        | 7.0   | 525  | 0.7806          | 0.5767   |
| 0.8438        | 8.0   | 600  | 0.7603          | 0.5833   |
| 0.812         | 9.0   | 675  | 0.7613          | 0.595    |
| 0.7427        | 10.0  | 750  | 0.8115          | 0.5917   |
| 0.8147        | 11.0  | 825  | 0.7428          | 0.63     |
| 0.7859        | 12.0  | 900  | 0.7365          | 0.635    |
| 0.8142        | 13.0  | 975  | 0.7468          | 0.6033   |
| 0.7961        | 14.0  | 1050 | 0.7567          | 0.5983   |
| 0.6725        | 15.0  | 1125 | 0.7876          | 0.6067   |
| 0.7608        | 16.0  | 1200 | 0.7339          | 0.635    |
| 0.7146        | 17.0  | 1275 | 0.7178          | 0.645    |
| 0.6646        | 18.0  | 1350 | 0.7089          | 0.67     |
| 0.7767        | 19.0  | 1425 | 0.7436          | 0.6433   |
| 0.7149        | 20.0  | 1500 | 0.7664          | 0.655    |
| 0.7622        | 21.0  | 1575 | 0.7227          | 0.6617   |
| 0.6643        | 22.0  | 1650 | 0.7547          | 0.64     |
| 0.7546        | 23.0  | 1725 | 0.7439          | 0.6483   |
| 0.727         | 24.0  | 1800 | 0.7101          | 0.6633   |
| 0.7334        | 25.0  | 1875 | 0.7022          | 0.6583   |
| 0.6824        | 26.0  | 1950 | 0.7040          | 0.6767   |
| 0.7383        | 27.0  | 2025 | 0.6953          | 0.6733   |
| 0.6459        | 28.0  | 2100 | 0.6860          | 0.6883   |
| 0.7094        | 29.0  | 2175 | 0.6882          | 0.695    |
| 0.7817        | 30.0  | 2250 | 0.6855          | 0.6883   |
| 0.6417        | 31.0  | 2325 | 0.6762          | 0.705    |
| 0.7236        | 32.0  | 2400 | 0.6870          | 0.6917   |
| 0.6676        | 33.0  | 2475 | 0.7290          | 0.685    |
| 0.5839        | 34.0  | 2550 | 0.6648          | 0.7117   |
| 0.6323        | 35.0  | 2625 | 0.6543          | 0.7017   |
| 0.6129        | 36.0  | 2700 | 0.6910          | 0.6883   |
| 0.5785        | 37.0  | 2775 | 0.6666          | 0.7217   |
| 0.6055        | 38.0  | 2850 | 0.6452          | 0.7233   |
| 0.5778        | 39.0  | 2925 | 0.6586          | 0.7217   |
| 0.5892        | 40.0  | 3000 | 0.6725          | 0.7233   |
| 0.6346        | 41.0  | 3075 | 0.6632          | 0.715    |
| 0.5806        | 42.0  | 3150 | 0.6697          | 0.7217   |
| 0.6328        | 43.0  | 3225 | 0.6659          | 0.7117   |
| 0.5711        | 44.0  | 3300 | 0.6651          | 0.71     |
| 0.5685        | 45.0  | 3375 | 0.6727          | 0.7283   |
| 0.4903        | 46.0  | 3450 | 0.6607          | 0.7383   |
| 0.5197        | 47.0  | 3525 | 0.6770          | 0.7283   |
| 0.5572        | 48.0  | 3600 | 0.6616          | 0.7183   |
| 0.5197        | 49.0  | 3675 | 0.6636          | 0.73     |
| 0.489         | 50.0  | 3750 | 0.6670          | 0.7333   |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-base-patch16-224", "model-index": [{"name": "smids_1x_beit_base_rms_0001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7333333333333333, "name": "Accuracy"}]}]}]}
transformers tensorboard safetensors beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us