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# SMIDS_5x_beit_large_Adamax_lr001_fold3

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: 1.2538
- 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6045        | 1.0   | 750   | 0.5123          | 0.7817   |
| 0.4468        | 2.0   | 1500  | 0.3821          | 0.8533   |
| 0.3305        | 3.0   | 2250  | 0.4003          | 0.845    |
| 0.3849        | 4.0   | 3000  | 0.4319          | 0.8517   |
| 0.4109        | 5.0   | 3750  | 0.3346          | 0.875    |
| 0.2443        | 6.0   | 4500  | 0.3478          | 0.8767   |
| 0.2134        | 7.0   | 5250  | 0.4074          | 0.8667   |
| 0.2183        | 8.0   | 6000  | 0.4455          | 0.8767   |
| 0.2511        | 9.0   | 6750  | 0.5282          | 0.85     |
| 0.1019        | 10.0  | 7500  | 0.4915          | 0.8517   |
| 0.1586        | 11.0  | 8250  | 0.5501          | 0.8717   |
| 0.1109        | 12.0  | 9000  | 0.5930          | 0.86     |
| 0.0976        | 13.0  | 9750  | 0.6096          | 0.86     |
| 0.1208        | 14.0  | 10500 | 0.5831          | 0.8867   |
| 0.2436        | 15.0  | 11250 | 0.8099          | 0.865    |
| 0.0658        | 16.0  | 12000 | 0.7428          | 0.8683   |
| 0.0768        | 17.0  | 12750 | 0.7750          | 0.8667   |
| 0.0607        | 18.0  | 13500 | 0.7753          | 0.8667   |
| 0.0356        | 19.0  | 14250 | 0.8640          | 0.88     |
| 0.0613        | 20.0  | 15000 | 0.8615          | 0.86     |
| 0.0219        | 21.0  | 15750 | 0.8772          | 0.8717   |
| 0.0052        | 22.0  | 16500 | 0.7802          | 0.8767   |
| 0.0039        | 23.0  | 17250 | 0.8166          | 0.87     |
| 0.0404        | 24.0  | 18000 | 0.9244          | 0.8633   |
| 0.0131        | 25.0  | 18750 | 0.8952          | 0.8633   |
| 0.0115        | 26.0  | 19500 | 0.9535          | 0.8817   |
| 0.0002        | 27.0  | 20250 | 0.9097          | 0.8733   |
| 0.0           | 28.0  | 21000 | 1.0511          | 0.885    |
| 0.0105        | 29.0  | 21750 | 1.0369          | 0.8867   |
| 0.0001        | 30.0  | 22500 | 1.0169          | 0.8767   |
| 0.0002        | 31.0  | 23250 | 1.0531          | 0.8833   |
| 0.0002        | 32.0  | 24000 | 1.0413          | 0.8833   |
| 0.0002        | 33.0  | 24750 | 1.0651          | 0.88     |
| 0.0           | 34.0  | 25500 | 1.2403          | 0.8733   |
| 0.0           | 35.0  | 26250 | 1.1424          | 0.8817   |
| 0.0056        | 36.0  | 27000 | 1.2619          | 0.8833   |
| 0.0272        | 37.0  | 27750 | 1.2435          | 0.8717   |
| 0.0           | 38.0  | 28500 | 1.0638          | 0.88     |
| 0.0           | 39.0  | 29250 | 1.1689          | 0.8783   |
| 0.0           | 40.0  | 30000 | 1.1136          | 0.875    |
| 0.0           | 41.0  | 30750 | 1.3121          | 0.8733   |
| 0.0           | 42.0  | 31500 | 1.3839          | 0.87     |
| 0.0           | 43.0  | 32250 | 1.2245          | 0.885    |
| 0.0001        | 44.0  | 33000 | 1.1671          | 0.8767   |
| 0.0001        | 45.0  | 33750 | 1.2221          | 0.8783   |
| 0.0           | 46.0  | 34500 | 1.2081          | 0.8817   |
| 0.0           | 47.0  | 35250 | 1.2288          | 0.88     |
| 0.0           | 48.0  | 36000 | 1.2426          | 0.8833   |
| 0.0           | 49.0  | 36750 | 1.2580          | 0.875    |
| 0.0           | 50.0  | 37500 | 1.2538          | 0.8817   |


### 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_Adamax_lr001_fold3", "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 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