
<!-- 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_RMSProp_lr001_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: 0.9467
- Accuracy: 0.7467

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8743        | 1.0   | 150  | 0.9241          | 0.51     |
| 0.9471        | 2.0   | 300  | 0.8991          | 0.5017   |
| 0.8335        | 3.0   | 450  | 0.8577          | 0.5483   |
| 0.8559        | 4.0   | 600  | 0.8348          | 0.5433   |
| 0.771         | 5.0   | 750  | 0.8265          | 0.555    |
| 0.7534        | 6.0   | 900  | 0.8230          | 0.5483   |
| 0.8493        | 7.0   | 1050 | 0.7999          | 0.56     |
| 0.787         | 8.0   | 1200 | 0.8000          | 0.5933   |
| 0.7078        | 9.0   | 1350 | 0.7904          | 0.5783   |
| 0.7654        | 10.0  | 1500 | 0.7641          | 0.635    |
| 0.7743        | 11.0  | 1650 | 0.7144          | 0.67     |
| 0.7923        | 12.0  | 1800 | 0.7159          | 0.6583   |
| 0.6966        | 13.0  | 1950 | 0.7248          | 0.6617   |
| 0.6186        | 14.0  | 2100 | 0.6956          | 0.6783   |
| 0.6328        | 15.0  | 2250 | 0.7104          | 0.665    |
| 0.7029        | 16.0  | 2400 | 0.7017          | 0.6667   |
| 0.6026        | 17.0  | 2550 | 0.6894          | 0.6567   |
| 0.6458        | 18.0  | 2700 | 0.6954          | 0.6867   |
| 0.6806        | 19.0  | 2850 | 0.6768          | 0.6867   |
| 0.5242        | 20.0  | 3000 | 0.6829          | 0.7      |
| 0.6007        | 21.0  | 3150 | 0.6819          | 0.6817   |
| 0.6452        | 22.0  | 3300 | 0.6625          | 0.6967   |
| 0.5123        | 23.0  | 3450 | 0.7786          | 0.6717   |
| 0.6237        | 24.0  | 3600 | 0.6740          | 0.7017   |
| 0.5702        | 25.0  | 3750 | 0.6545          | 0.71     |
| 0.5835        | 26.0  | 3900 | 0.6422          | 0.72     |
| 0.5456        | 27.0  | 4050 | 0.6530          | 0.7033   |
| 0.5534        | 28.0  | 4200 | 0.6917          | 0.6933   |
| 0.599         | 29.0  | 4350 | 0.6625          | 0.6867   |
| 0.5575        | 30.0  | 4500 | 0.6193          | 0.7133   |
| 0.4903        | 31.0  | 4650 | 0.6206          | 0.7483   |
| 0.4733        | 32.0  | 4800 | 0.6712          | 0.7233   |
| 0.4453        | 33.0  | 4950 | 0.6585          | 0.7083   |
| 0.4398        | 34.0  | 5100 | 0.6308          | 0.7267   |
| 0.4656        | 35.0  | 5250 | 0.6706          | 0.7317   |
| 0.4063        | 36.0  | 5400 | 0.7565          | 0.6967   |
| 0.4655        | 37.0  | 5550 | 0.6287          | 0.7333   |
| 0.4692        | 38.0  | 5700 | 0.6492          | 0.7283   |
| 0.3583        | 39.0  | 5850 | 0.6919          | 0.735    |
| 0.3685        | 40.0  | 6000 | 0.6859          | 0.7367   |
| 0.3705        | 41.0  | 6150 | 0.7082          | 0.7467   |
| 0.4699        | 42.0  | 6300 | 0.7217          | 0.735    |
| 0.2786        | 43.0  | 6450 | 0.8041          | 0.73     |
| 0.2864        | 44.0  | 6600 | 0.7402          | 0.745    |
| 0.3797        | 45.0  | 6750 | 0.7223          | 0.745    |
| 0.2363        | 46.0  | 6900 | 0.8103          | 0.7417   |
| 0.2456        | 47.0  | 7050 | 0.8541          | 0.7517   |
| 0.1837        | 48.0  | 7200 | 0.9317          | 0.7533   |
| 0.2568        | 49.0  | 7350 | 0.9312          | 0.745    |
| 0.1837        | 50.0  | 7500 | 0.9467          | 0.7467   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1

{"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_RMSProp_lr001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7466666666666667, "name": "Accuracy"}]}]}]}
transformers safetensors 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