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# SMIDS_10x_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: 1.9318
- Accuracy: 0.8817

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3082        | 1.0   | 1500  | 1.0037          | 0.6233   |
| 0.2982        | 2.0   | 3000  | 0.3757          | 0.8667   |
| 0.2923        | 3.0   | 4500  | 0.4293          | 0.8683   |
| 0.153         | 4.0   | 6000  | 0.4779          | 0.855    |
| 0.1259        | 5.0   | 7500  | 0.5782          | 0.8567   |
| 0.0674        | 6.0   | 9000  | 0.8138          | 0.8367   |
| 0.1132        | 7.0   | 10500 | 0.6177          | 0.8617   |
| 0.0492        | 8.0   | 12000 | 0.7625          | 0.875    |
| 0.0278        | 9.0   | 13500 | 0.6558          | 0.8833   |
| 0.0173        | 10.0  | 15000 | 0.8245          | 0.8817   |
| 0.0014        | 11.0  | 16500 | 0.8559          | 0.8633   |
| 0.1141        | 12.0  | 18000 | 0.6787          | 0.88     |
| 0.0114        | 13.0  | 19500 | 0.9914          | 0.8667   |
| 0.0011        | 14.0  | 21000 | 0.8511          | 0.8683   |
| 0.0554        | 15.0  | 22500 | 1.1418          | 0.85     |
| 0.0005        | 16.0  | 24000 | 0.9714          | 0.8767   |
| 0.0343        | 17.0  | 25500 | 0.9018          | 0.8567   |
| 0.0018        | 18.0  | 27000 | 0.9980          | 0.865    |
| 0.0008        | 19.0  | 28500 | 0.8771          | 0.8733   |
| 0.0461        | 20.0  | 30000 | 0.7565          | 0.8733   |
| 0.0409        | 21.0  | 31500 | 1.0630          | 0.8767   |
| 0.0008        | 22.0  | 33000 | 1.0187          | 0.8767   |
| 0.0199        | 23.0  | 34500 | 1.0438          | 0.8683   |
| 0.0001        | 24.0  | 36000 | 1.0376          | 0.8717   |
| 0.0002        | 25.0  | 37500 | 1.0980          | 0.865    |
| 0.0368        | 26.0  | 39000 | 1.0182          | 0.8767   |
| 0.0001        | 27.0  | 40500 | 1.0284          | 0.8783   |
| 0.0002        | 28.0  | 42000 | 1.1545          | 0.87     |
| 0.0           | 29.0  | 43500 | 1.0723          | 0.8783   |
| 0.0003        | 30.0  | 45000 | 1.0848          | 0.8817   |
| 0.0           | 31.0  | 46500 | 1.0750          | 0.8783   |
| 0.0001        | 32.0  | 48000 | 1.2046          | 0.885    |
| 0.0218        | 33.0  | 49500 | 1.1655          | 0.87     |
| 0.0           | 34.0  | 51000 | 1.2777          | 0.865    |
| 0.0005        | 35.0  | 52500 | 1.0834          | 0.8767   |
| 0.0008        | 36.0  | 54000 | 1.3497          | 0.865    |
| 0.0           | 37.0  | 55500 | 1.3092          | 0.8867   |
| 0.0           | 38.0  | 57000 | 1.4072          | 0.8833   |
| 0.0           | 39.0  | 58500 | 1.7298          | 0.8783   |
| 0.0           | 40.0  | 60000 | 1.5101          | 0.8733   |
| 0.0004        | 41.0  | 61500 | 1.7295          | 0.8783   |
| 0.0           | 42.0  | 63000 | 1.5437          | 0.8833   |
| 0.0           | 43.0  | 64500 | 1.6784          | 0.8833   |
| 0.0           | 44.0  | 66000 | 1.7613          | 0.885    |
| 0.0           | 45.0  | 67500 | 1.7636          | 0.89     |
| 0.0           | 46.0  | 69000 | 1.9303          | 0.88     |
| 0.0           | 47.0  | 70500 | 1.8625          | 0.8833   |
| 0.0           | 48.0  | 72000 | 1.9108          | 0.88     |
| 0.0           | 49.0  | 73500 | 1.9041          | 0.8817   |
| 0.0           | 50.0  | 75000 | 1.9318          | 0.8817   |


### 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_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": 0.8816666666666667, "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