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# SMIDS_5x_beit_large_RMSProp_lr0001_fold4

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: 1.6009
- Accuracy: 0.8883

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4658        | 1.0   | 750   | 0.4527          | 0.82     |
| 0.2776        | 2.0   | 1500  | 0.3736          | 0.8617   |
| 0.2473        | 3.0   | 2250  | 0.4142          | 0.86     |
| 0.2012        | 4.0   | 3000  | 0.5259          | 0.855    |
| 0.0839        | 5.0   | 3750  | 0.4873          | 0.875    |
| 0.1025        | 6.0   | 4500  | 0.7847          | 0.8283   |
| 0.1125        | 7.0   | 5250  | 0.6266          | 0.875    |
| 0.1011        | 8.0   | 6000  | 0.7642          | 0.855    |
| 0.0518        | 9.0   | 6750  | 0.7001          | 0.8633   |
| 0.184         | 10.0  | 7500  | 0.7421          | 0.8667   |
| 0.0756        | 11.0  | 8250  | 0.7752          | 0.8717   |
| 0.1104        | 12.0  | 9000  | 0.8456          | 0.855    |
| 0.0341        | 13.0  | 9750  | 0.7640          | 0.8717   |
| 0.0888        | 14.0  | 10500 | 0.6925          | 0.8567   |
| 0.0436        | 15.0  | 11250 | 0.7861          | 0.87     |
| 0.007         | 16.0  | 12000 | 0.9471          | 0.86     |
| 0.0626        | 17.0  | 12750 | 1.1129          | 0.845    |
| 0.0033        | 18.0  | 13500 | 1.0612          | 0.8617   |
| 0.0659        | 19.0  | 14250 | 0.9212          | 0.86     |
| 0.0155        | 20.0  | 15000 | 1.0544          | 0.865    |
| 0.0244        | 21.0  | 15750 | 1.1542          | 0.8483   |
| 0.0439        | 22.0  | 16500 | 0.9552          | 0.86     |
| 0.0042        | 23.0  | 17250 | 1.1753          | 0.865    |
| 0.0138        | 24.0  | 18000 | 1.2700          | 0.845    |
| 0.053         | 25.0  | 18750 | 1.1634          | 0.86     |
| 0.0009        | 26.0  | 19500 | 1.1270          | 0.865    |
| 0.0352        | 27.0  | 20250 | 1.3002          | 0.85     |
| 0.0           | 28.0  | 21000 | 1.0522          | 0.875    |
| 0.0002        | 29.0  | 21750 | 1.3199          | 0.8667   |
| 0.0476        | 30.0  | 22500 | 0.9340          | 0.8833   |
| 0.0001        | 31.0  | 23250 | 1.1098          | 0.8717   |
| 0.0254        | 32.0  | 24000 | 1.2110          | 0.86     |
| 0.0313        | 33.0  | 24750 | 1.2025          | 0.86     |
| 0.0           | 34.0  | 25500 | 1.2413          | 0.8783   |
| 0.0001        | 35.0  | 26250 | 1.0715          | 0.8783   |
| 0.0002        | 36.0  | 27000 | 1.2316          | 0.8783   |
| 0.0           | 37.0  | 27750 | 1.4479          | 0.8683   |
| 0.0           | 38.0  | 28500 | 1.3997          | 0.8817   |
| 0.0           | 39.0  | 29250 | 1.4936          | 0.8767   |
| 0.0           | 40.0  | 30000 | 1.5416          | 0.8783   |
| 0.0           | 41.0  | 30750 | 1.4632          | 0.8833   |
| 0.0           | 42.0  | 31500 | 1.4775          | 0.88     |
| 0.0           | 43.0  | 32250 | 1.4782          | 0.885    |
| 0.0           | 44.0  | 33000 | 1.4608          | 0.8817   |
| 0.0           | 45.0  | 33750 | 1.4785          | 0.8867   |
| 0.0           | 46.0  | 34500 | 1.5409          | 0.8833   |
| 0.0           | 47.0  | 35250 | 1.5927          | 0.8883   |
| 0.0           | 48.0  | 36000 | 1.5923          | 0.8883   |
| 0.0           | 49.0  | 36750 | 1.5981          | 0.8883   |
| 0.0           | 50.0  | 37500 | 1.6009          | 0.8883   |


### 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_5x_beit_large_RMSProp_lr0001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8883333333333333, "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