
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# SMIDS_1x_beit_large_Adamax_lr001_fold5

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: 1.7618
- Accuracy: 0.7917

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0286        | 1.0   | 150  | 0.8282          | 0.5617   |
| 0.8229        | 2.0   | 300  | 0.7821          | 0.6083   |
| 0.8429        | 3.0   | 450  | 0.8035          | 0.5667   |
| 0.9308        | 4.0   | 600  | 0.8515          | 0.55     |
| 0.7431        | 5.0   | 750  | 0.8044          | 0.6017   |
| 0.7021        | 6.0   | 900  | 0.8640          | 0.5933   |
| 0.8198        | 7.0   | 1050 | 0.7854          | 0.5733   |
| 0.7019        | 8.0   | 1200 | 0.7941          | 0.63     |
| 0.6544        | 9.0   | 1350 | 0.7384          | 0.6633   |
| 0.6385        | 10.0  | 1500 | 0.7255          | 0.6917   |
| 0.6759        | 11.0  | 1650 | 0.6975          | 0.6683   |
| 0.6462        | 12.0  | 1800 | 0.6623          | 0.715    |
| 0.655         | 13.0  | 1950 | 0.7598          | 0.6583   |
| 0.5197        | 14.0  | 2100 | 0.6357          | 0.7217   |
| 0.5891        | 15.0  | 2250 | 0.6186          | 0.73     |
| 0.638         | 16.0  | 2400 | 0.6122          | 0.7317   |
| 0.5255        | 17.0  | 2550 | 0.6692          | 0.7183   |
| 0.548         | 18.0  | 2700 | 0.5865          | 0.7433   |
| 0.4565        | 19.0  | 2850 | 0.5449          | 0.7717   |
| 0.4218        | 20.0  | 3000 | 0.6183          | 0.75     |
| 0.5097        | 21.0  | 3150 | 0.6142          | 0.75     |
| 0.4003        | 22.0  | 3300 | 0.5927          | 0.7717   |
| 0.3721        | 23.0  | 3450 | 0.6473          | 0.7517   |
| 0.4336        | 24.0  | 3600 | 0.6669          | 0.78     |
| 0.3002        | 25.0  | 3750 | 0.6705          | 0.7667   |
| 0.259         | 26.0  | 3900 | 0.7635          | 0.77     |
| 0.2578        | 27.0  | 4050 | 0.7830          | 0.78     |
| 0.3265        | 28.0  | 4200 | 0.8561          | 0.775    |
| 0.2446        | 29.0  | 4350 | 1.0922          | 0.765    |
| 0.2295        | 30.0  | 4500 | 0.8527          | 0.79     |
| 0.0989        | 31.0  | 4650 | 1.0274          | 0.7733   |
| 0.072         | 32.0  | 4800 | 1.0451          | 0.78     |
| 0.1457        | 33.0  | 4950 | 1.3056          | 0.7467   |
| 0.2145        | 34.0  | 5100 | 1.1509          | 0.7833   |
| 0.123         | 35.0  | 5250 | 1.5634          | 0.7717   |
| 0.0831        | 36.0  | 5400 | 1.5582          | 0.7733   |
| 0.0625        | 37.0  | 5550 | 1.9293          | 0.785    |
| 0.0365        | 38.0  | 5700 | 1.6260          | 0.785    |
| 0.026         | 39.0  | 5850 | 1.7152          | 0.7933   |
| 0.058         | 40.0  | 6000 | 1.8594          | 0.78     |
| 0.0882        | 41.0  | 6150 | 1.9281          | 0.7767   |
| 0.0542        | 42.0  | 6300 | 1.7842          | 0.7833   |
| 0.096         | 43.0  | 6450 | 1.8541          | 0.7883   |
| 0.0031        | 44.0  | 6600 | 1.7815          | 0.7883   |
| 0.0005        | 45.0  | 6750 | 1.8569          | 0.785    |
| 0.0454        | 46.0  | 6900 | 1.7846          | 0.7883   |
| 0.0035        | 47.0  | 7050 | 1.7384          | 0.7833   |
| 0.001         | 48.0  | 7200 | 1.7514          | 0.795    |
| 0.0409        | 49.0  | 7350 | 1.7549          | 0.7933   |
| 0.0002        | 50.0  | 7500 | 1.7618          | 0.7917   |


### 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_1x_beit_large_Adamax_lr001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7916666666666666, "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