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# SMIDS_5x_beit_large_SGD_lr00001_fold3

This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8501
- Accuracy: 0.6

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.5966        | 1.0   | 750   | 1.6087          | 0.29     |
| 1.4698        | 2.0   | 1500  | 1.5127          | 0.2917   |
| 1.4335        | 3.0   | 2250  | 1.4292          | 0.285    |
| 1.3693        | 4.0   | 3000  | 1.3566          | 0.2767   |
| 1.3019        | 5.0   | 3750  | 1.2940          | 0.2933   |
| 1.3122        | 6.0   | 4500  | 1.2396          | 0.3      |
| 1.2257        | 7.0   | 5250  | 1.1932          | 0.3333   |
| 1.2221        | 8.0   | 6000  | 1.1542          | 0.3767   |
| 1.1758        | 9.0   | 6750  | 1.1217          | 0.4217   |
| 1.1316        | 10.0  | 7500  | 1.0946          | 0.445    |
| 1.0921        | 11.0  | 8250  | 1.0716          | 0.4417   |
| 1.0552        | 12.0  | 9000  | 1.0518          | 0.4583   |
| 0.9755        | 13.0  | 9750  | 1.0346          | 0.48     |
| 1.0236        | 14.0  | 10500 | 1.0194          | 0.4917   |
| 0.9697        | 15.0  | 11250 | 1.0059          | 0.5017   |
| 1.0185        | 16.0  | 12000 | 0.9936          | 0.5117   |
| 0.9935        | 17.0  | 12750 | 0.9824          | 0.515    |
| 0.9449        | 18.0  | 13500 | 0.9721          | 0.5217   |
| 0.9563        | 19.0  | 14250 | 0.9626          | 0.53     |
| 0.9929        | 20.0  | 15000 | 0.9538          | 0.5383   |
| 0.9315        | 21.0  | 15750 | 0.9456          | 0.5467   |
| 0.9688        | 22.0  | 16500 | 0.9378          | 0.555    |
| 0.9337        | 23.0  | 17250 | 0.9304          | 0.5567   |
| 0.849         | 24.0  | 18000 | 0.9236          | 0.56     |
| 0.9164        | 25.0  | 18750 | 0.9172          | 0.5617   |
| 0.8443        | 26.0  | 19500 | 0.9112          | 0.565    |
| 0.8812        | 27.0  | 20250 | 0.9055          | 0.5633   |
| 0.7952        | 28.0  | 21000 | 0.9001          | 0.57     |
| 0.8245        | 29.0  | 21750 | 0.8951          | 0.57     |
| 0.8389        | 30.0  | 22500 | 0.8904          | 0.5733   |
| 0.9509        | 31.0  | 23250 | 0.8860          | 0.575    |
| 0.8733        | 32.0  | 24000 | 0.8820          | 0.58     |
| 0.8935        | 33.0  | 24750 | 0.8782          | 0.5833   |
| 0.8504        | 34.0  | 25500 | 0.8747          | 0.5883   |
| 0.8893        | 35.0  | 26250 | 0.8714          | 0.59     |
| 0.8104        | 36.0  | 27000 | 0.8684          | 0.5917   |
| 0.8083        | 37.0  | 27750 | 0.8657          | 0.5917   |
| 0.8697        | 38.0  | 28500 | 0.8632          | 0.595    |
| 0.8683        | 39.0  | 29250 | 0.8609          | 0.595    |
| 0.8772        | 40.0  | 30000 | 0.8589          | 0.5967   |
| 0.8313        | 41.0  | 30750 | 0.8571          | 0.5967   |
| 0.7938        | 42.0  | 31500 | 0.8556          | 0.5983   |
| 0.8649        | 43.0  | 32250 | 0.8542          | 0.5983   |
| 0.8715        | 44.0  | 33000 | 0.8530          | 0.5983   |
| 0.8891        | 45.0  | 33750 | 0.8521          | 0.6      |
| 0.7769        | 46.0  | 34500 | 0.8513          | 0.6      |
| 0.7386        | 47.0  | 35250 | 0.8508          | 0.6      |
| 0.8308        | 48.0  | 36000 | 0.8504          | 0.6      |
| 0.8487        | 49.0  | 36750 | 0.8501          | 0.6      |
| 0.7938        | 50.0  | 37500 | 0.8501          | 0.6      |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_5x_beit_large_SGD_lr00001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6, "name": "Accuracy"}]}]}]}
transformers pytorch beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-large-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us