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# smids_1x_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: 0.9358
- Accuracy: 0.7404

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0437        | 1.0   | 75   | 0.9679          | 0.5042   |
| 0.9234        | 2.0   | 150  | 0.8669          | 0.5208   |
| 1.0795        | 3.0   | 225  | 0.7926          | 0.5874   |
| 0.9543        | 4.0   | 300  | 0.8244          | 0.5507   |
| 0.8239        | 5.0   | 375  | 0.7959          | 0.5857   |
| 0.7924        | 6.0   | 450  | 0.7928          | 0.5890   |
| 0.8468        | 7.0   | 525  | 0.7806          | 0.6256   |
| 0.8608        | 8.0   | 600  | 0.9027          | 0.5408   |
| 0.7878        | 9.0   | 675  | 0.7544          | 0.6373   |
| 0.9079        | 10.0  | 750  | 0.7732          | 0.6190   |
| 0.7705        | 11.0  | 825  | 0.7349          | 0.6290   |
| 0.7586        | 12.0  | 900  | 0.7322          | 0.6306   |
| 0.7794        | 13.0  | 975  | 0.7224          | 0.6323   |
| 0.7123        | 14.0  | 1050 | 0.7252          | 0.6572   |
| 0.744         | 15.0  | 1125 | 0.7450          | 0.5990   |
| 0.7086        | 16.0  | 1200 | 0.6962          | 0.6639   |
| 0.7295        | 17.0  | 1275 | 0.7508          | 0.6489   |
| 0.7289        | 18.0  | 1350 | 0.6978          | 0.6722   |
| 0.6947        | 19.0  | 1425 | 0.7112          | 0.6739   |
| 0.6923        | 20.0  | 1500 | 0.7131          | 0.6805   |
| 0.7545        | 21.0  | 1575 | 0.7480          | 0.6223   |
| 0.68          | 22.0  | 1650 | 0.6683          | 0.6839   |
| 0.7107        | 23.0  | 1725 | 0.6889          | 0.6772   |
| 0.6933        | 24.0  | 1800 | 0.6566          | 0.6822   |
| 0.6429        | 25.0  | 1875 | 0.6381          | 0.7005   |
| 0.6742        | 26.0  | 1950 | 0.6536          | 0.6822   |
| 0.6753        | 27.0  | 2025 | 0.6462          | 0.6889   |
| 0.6228        | 28.0  | 2100 | 0.6368          | 0.7022   |
| 0.6193        | 29.0  | 2175 | 0.6115          | 0.7171   |
| 0.5568        | 30.0  | 2250 | 0.6625          | 0.7188   |
| 0.584         | 31.0  | 2325 | 0.6680          | 0.6922   |
| 0.581         | 32.0  | 2400 | 0.5723          | 0.7654   |
| 0.5698        | 33.0  | 2475 | 0.6173          | 0.7205   |
| 0.5032        | 34.0  | 2550 | 0.6176          | 0.7338   |
| 0.5019        | 35.0  | 2625 | 0.6137          | 0.7438   |
| 0.4921        | 36.0  | 2700 | 0.5855          | 0.7571   |
| 0.453         | 37.0  | 2775 | 0.6724          | 0.7271   |
| 0.4913        | 38.0  | 2850 | 0.6043          | 0.7720   |
| 0.3871        | 39.0  | 2925 | 0.6124          | 0.7704   |
| 0.4014        | 40.0  | 3000 | 0.6591          | 0.7521   |
| 0.4698        | 41.0  | 3075 | 0.6575          | 0.7604   |
| 0.375         | 42.0  | 3150 | 0.6735          | 0.7471   |
| 0.317         | 43.0  | 3225 | 0.7867          | 0.7504   |
| 0.2968        | 44.0  | 3300 | 0.7423          | 0.7521   |
| 0.2919        | 45.0  | 3375 | 0.8253          | 0.7504   |
| 0.2598        | 46.0  | 3450 | 0.8629          | 0.7421   |
| 0.1951        | 47.0  | 3525 | 0.8586          | 0.7704   |
| 0.1905        | 48.0  | 3600 | 0.9010          | 0.7438   |
| 0.1278        | 49.0  | 3675 | 0.9354          | 0.7454   |
| 0.2294        | 50.0  | 3750 | 0.9358          | 0.7404   |


### 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_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.740432612312812, "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