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# SMIDS_10x_beit_large_SGD_lr001_fold2

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: 0.3719
- Accuracy: 0.9085

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3669        | 1.0   | 1500  | 0.3531          | 0.8486   |
| 0.2643        | 2.0   | 3000  | 0.2974          | 0.8702   |
| 0.21          | 3.0   | 4500  | 0.2781          | 0.8869   |
| 0.3676        | 4.0   | 6000  | 0.2708          | 0.8869   |
| 0.2251        | 5.0   | 7500  | 0.2708          | 0.8952   |
| 0.1554        | 6.0   | 9000  | 0.2699          | 0.8918   |
| 0.2477        | 7.0   | 10500 | 0.2696          | 0.9035   |
| 0.1426        | 8.0   | 12000 | 0.2686          | 0.8985   |
| 0.1794        | 9.0   | 13500 | 0.2695          | 0.9018   |
| 0.1581        | 10.0  | 15000 | 0.2764          | 0.9052   |
| 0.1971        | 11.0  | 16500 | 0.2764          | 0.9035   |
| 0.2049        | 12.0  | 18000 | 0.2796          | 0.9035   |
| 0.1908        | 13.0  | 19500 | 0.2841          | 0.9002   |
| 0.0932        | 14.0  | 21000 | 0.2867          | 0.9052   |
| 0.1566        | 15.0  | 22500 | 0.2893          | 0.9052   |
| 0.1526        | 16.0  | 24000 | 0.2943          | 0.9068   |
| 0.1138        | 17.0  | 25500 | 0.2953          | 0.9068   |
| 0.1111        | 18.0  | 27000 | 0.2967          | 0.9085   |
| 0.1188        | 19.0  | 28500 | 0.3025          | 0.9068   |
| 0.2174        | 20.0  | 30000 | 0.3066          | 0.9052   |
| 0.232         | 21.0  | 31500 | 0.3109          | 0.9085   |
| 0.1211        | 22.0  | 33000 | 0.3177          | 0.9101   |
| 0.0917        | 23.0  | 34500 | 0.3145          | 0.9118   |
| 0.1135        | 24.0  | 36000 | 0.3207          | 0.9085   |
| 0.0818        | 25.0  | 37500 | 0.3219          | 0.9101   |
| 0.2011        | 26.0  | 39000 | 0.3319          | 0.9068   |
| 0.2086        | 27.0  | 40500 | 0.3319          | 0.9052   |
| 0.082         | 28.0  | 42000 | 0.3312          | 0.9101   |
| 0.1554        | 29.0  | 43500 | 0.3353          | 0.9101   |
| 0.0755        | 30.0  | 45000 | 0.3372          | 0.9085   |
| 0.2336        | 31.0  | 46500 | 0.3406          | 0.9085   |
| 0.0946        | 32.0  | 48000 | 0.3426          | 0.9068   |
| 0.0676        | 33.0  | 49500 | 0.3471          | 0.9068   |
| 0.1596        | 34.0  | 51000 | 0.3486          | 0.9085   |
| 0.0868        | 35.0  | 52500 | 0.3486          | 0.9085   |
| 0.1018        | 36.0  | 54000 | 0.3578          | 0.9118   |
| 0.1158        | 37.0  | 55500 | 0.3544          | 0.9101   |
| 0.1257        | 38.0  | 57000 | 0.3586          | 0.9101   |
| 0.1282        | 39.0  | 58500 | 0.3620          | 0.9085   |
| 0.2047        | 40.0  | 60000 | 0.3608          | 0.9101   |
| 0.1026        | 41.0  | 61500 | 0.3628          | 0.9101   |
| 0.1079        | 42.0  | 63000 | 0.3671          | 0.9101   |
| 0.131         | 43.0  | 64500 | 0.3678          | 0.9101   |
| 0.0804        | 44.0  | 66000 | 0.3717          | 0.9085   |
| 0.1156        | 45.0  | 67500 | 0.3688          | 0.9101   |
| 0.2166        | 46.0  | 69000 | 0.3704          | 0.9068   |
| 0.2364        | 47.0  | 70500 | 0.3720          | 0.9068   |
| 0.0756        | 48.0  | 72000 | 0.3724          | 0.9068   |
| 0.0686        | 49.0  | 73500 | 0.3720          | 0.9101   |
| 0.0917        | 50.0  | 75000 | 0.3719          | 0.9085   |


### 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_10x_beit_large_SGD_lr001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.908485856905158, "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