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# SMIDS_1x_beit_large_RMSProp_lr0001_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: 1.7185
- Accuracy: 0.8319

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8977        | 1.0   | 150  | 0.8249          | 0.5607   |
| 0.769         | 2.0   | 300  | 0.8548          | 0.5824   |
| 0.5683        | 3.0   | 450  | 0.8321          | 0.5824   |
| 0.6138        | 4.0   | 600  | 0.6162          | 0.7438   |
| 0.5807        | 5.0   | 750  | 0.5974          | 0.7504   |
| 0.6004        | 6.0   | 900  | 0.5444          | 0.7587   |
| 0.5493        | 7.0   | 1050 | 0.5171          | 0.7920   |
| 0.5217        | 8.0   | 1200 | 0.5507          | 0.7854   |
| 0.4123        | 9.0   | 1350 | 0.8184          | 0.7404   |
| 0.3957        | 10.0  | 1500 | 0.6886          | 0.7454   |
| 0.2705        | 11.0  | 1650 | 0.7442          | 0.7837   |
| 0.3411        | 12.0  | 1800 | 0.5546          | 0.8020   |
| 0.2785        | 13.0  | 1950 | 0.5865          | 0.8003   |
| 0.3365        | 14.0  | 2100 | 0.5703          | 0.7937   |
| 0.2779        | 15.0  | 2250 | 0.5882          | 0.8153   |
| 0.1723        | 16.0  | 2400 | 0.7640          | 0.7870   |
| 0.2863        | 17.0  | 2550 | 0.6849          | 0.7953   |
| 0.2305        | 18.0  | 2700 | 0.7203          | 0.8103   |
| 0.1756        | 19.0  | 2850 | 0.7689          | 0.8153   |
| 0.1259        | 20.0  | 3000 | 0.8956          | 0.8053   |
| 0.1379        | 21.0  | 3150 | 0.8560          | 0.8236   |
| 0.1091        | 22.0  | 3300 | 1.2345          | 0.8087   |
| 0.1783        | 23.0  | 3450 | 1.2738          | 0.8087   |
| 0.203         | 24.0  | 3600 | 0.9764          | 0.7920   |
| 0.1033        | 25.0  | 3750 | 1.2051          | 0.8253   |
| 0.0381        | 26.0  | 3900 | 1.1853          | 0.8270   |
| 0.0312        | 27.0  | 4050 | 1.3057          | 0.8070   |
| 0.0816        | 28.0  | 4200 | 1.4736          | 0.8120   |
| 0.0164        | 29.0  | 4350 | 1.5045          | 0.8186   |
| 0.0435        | 30.0  | 4500 | 1.4291          | 0.8136   |
| 0.0805        | 31.0  | 4650 | 1.1793          | 0.8253   |
| 0.0444        | 32.0  | 4800 | 1.4372          | 0.8053   |
| 0.0021        | 33.0  | 4950 | 1.3723          | 0.8070   |
| 0.0573        | 34.0  | 5100 | 1.3775          | 0.8203   |
| 0.0008        | 35.0  | 5250 | 1.4740          | 0.8236   |
| 0.0008        | 36.0  | 5400 | 1.4771          | 0.8203   |
| 0.0004        | 37.0  | 5550 | 1.4577          | 0.8203   |
| 0.0002        | 38.0  | 5700 | 1.4710          | 0.8253   |
| 0.023         | 39.0  | 5850 | 1.3268          | 0.8136   |
| 0.0321        | 40.0  | 6000 | 1.4441          | 0.8270   |
| 0.0282        | 41.0  | 6150 | 1.5353          | 0.8220   |
| 0.014         | 42.0  | 6300 | 1.6130          | 0.8303   |
| 0.0018        | 43.0  | 6450 | 1.5574          | 0.8336   |
| 0.0001        | 44.0  | 6600 | 1.5359          | 0.8386   |
| 0.0266        | 45.0  | 6750 | 1.9501          | 0.8170   |
| 0.0           | 46.0  | 6900 | 1.7434          | 0.8336   |
| 0.0583        | 47.0  | 7050 | 1.5978          | 0.8386   |
| 0.035         | 48.0  | 7200 | 1.6144          | 0.8353   |
| 0.0119        | 49.0  | 7350 | 1.7086          | 0.8286   |
| 0.0445        | 50.0  | 7500 | 1.7185          | 0.8319   |


### 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_RMSProp_lr0001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.831946755407654, "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