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# SMIDS_10x_beit_large_rmsprop_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: 0.6367
- Accuracy: 0.7704

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7437        | 1.0   | 1500  | 1.5365          | 0.5075   |
| 0.7783        | 2.0   | 3000  | 0.8929          | 0.5757   |
| 0.7776        | 3.0   | 4500  | 0.7955          | 0.6007   |
| 0.8296        | 4.0   | 6000  | 0.7288          | 0.6639   |
| 0.7048        | 5.0   | 7500  | 0.7310          | 0.6323   |
| 0.8348        | 6.0   | 9000  | 0.7577          | 0.6406   |
| 0.8074        | 7.0   | 10500 | 0.7372          | 0.6556   |
| 0.7344        | 8.0   | 12000 | 0.7110          | 0.6622   |
| 0.6874        | 9.0   | 13500 | 0.6990          | 0.6922   |
| 0.6846        | 10.0  | 15000 | 0.6521          | 0.6972   |
| 0.6939        | 11.0  | 16500 | 0.6365          | 0.7105   |
| 0.709         | 12.0  | 18000 | 0.6521          | 0.7005   |
| 0.7084        | 13.0  | 19500 | 0.6723          | 0.7321   |
| 0.515         | 14.0  | 21000 | 0.6889          | 0.7038   |
| 0.5152        | 15.0  | 22500 | 0.6723          | 0.7055   |
| 0.5919        | 16.0  | 24000 | 0.6317          | 0.7587   |
| 0.4136        | 17.0  | 25500 | 0.6616          | 0.7438   |
| 0.5793        | 18.0  | 27000 | 0.6112          | 0.7471   |
| 0.5744        | 19.0  | 28500 | 0.7143          | 0.6689   |
| 0.5529        | 20.0  | 30000 | 0.6361          | 0.7454   |
| 0.6786        | 21.0  | 31500 | 0.5916          | 0.7587   |
| 0.5647        | 22.0  | 33000 | 0.5668          | 0.7587   |
| 0.5953        | 23.0  | 34500 | 0.6223          | 0.7388   |
| 0.5213        | 24.0  | 36000 | 0.5868          | 0.7671   |
| 0.5282        | 25.0  | 37500 | 0.6307          | 0.7321   |
| 0.4927        | 26.0  | 39000 | 0.5588          | 0.7737   |
| 0.4395        | 27.0  | 40500 | 0.5548          | 0.7654   |
| 0.5921        | 28.0  | 42000 | 0.5507          | 0.7637   |
| 0.519         | 29.0  | 43500 | 0.5523          | 0.7604   |
| 0.5953        | 30.0  | 45000 | 0.6039          | 0.7587   |
| 0.4593        | 31.0  | 46500 | 0.5937          | 0.7671   |
| 0.4407        | 32.0  | 48000 | 0.5905          | 0.7787   |
| 0.4331        | 33.0  | 49500 | 0.5357          | 0.7837   |
| 0.4162        | 34.0  | 51000 | 0.5840          | 0.7488   |
| 0.6042        | 35.0  | 52500 | 0.5734          | 0.7454   |
| 0.5193        | 36.0  | 54000 | 0.5797          | 0.7687   |
| 0.4934        | 37.0  | 55500 | 0.5532          | 0.7770   |
| 0.4305        | 38.0  | 57000 | 0.5996          | 0.7637   |
| 0.379         | 39.0  | 58500 | 0.6151          | 0.7754   |
| 0.5415        | 40.0  | 60000 | 0.5723          | 0.7671   |
| 0.4809        | 41.0  | 61500 | 0.5716          | 0.7754   |
| 0.4363        | 42.0  | 63000 | 0.5882          | 0.7687   |
| 0.4654        | 43.0  | 64500 | 0.5900          | 0.7654   |
| 0.4378        | 44.0  | 66000 | 0.6279          | 0.7671   |
| 0.3407        | 45.0  | 67500 | 0.6295          | 0.7604   |
| 0.5161        | 46.0  | 69000 | 0.6079          | 0.7637   |
| 0.3903        | 47.0  | 70500 | 0.6185          | 0.7737   |
| 0.4785        | 48.0  | 72000 | 0.5934          | 0.7704   |
| 0.3265        | 49.0  | 73500 | 0.6273          | 0.7637   |
| 0.3475        | 50.0  | 75000 | 0.6367          | 0.7704   |


### 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_rmsprop_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": 0.7703826955074875, "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