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# smids_3x_beit_base_adamax_00001_fold4

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0796
- 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2789        | 1.0   | 375   | 0.3658          | 0.8517   |
| 0.2051        | 2.0   | 750   | 0.3746          | 0.855    |
| 0.0849        | 3.0   | 1125  | 0.3986          | 0.87     |
| 0.1357        | 4.0   | 1500  | 0.4367          | 0.8633   |
| 0.0524        | 5.0   | 1875  | 0.4518          | 0.8867   |
| 0.0622        | 6.0   | 2250  | 0.5510          | 0.8867   |
| 0.0413        | 7.0   | 2625  | 0.6374          | 0.8767   |
| 0.0498        | 8.0   | 3000  | 0.6614          | 0.88     |
| 0.0181        | 9.0   | 3375  | 0.6937          | 0.8867   |
| 0.013         | 10.0  | 3750  | 0.7663          | 0.8817   |
| 0.0051        | 11.0  | 4125  | 0.8001          | 0.8783   |
| 0.0188        | 12.0  | 4500  | 0.8577          | 0.8817   |
| 0.0009        | 13.0  | 4875  | 0.8978          | 0.8783   |
| 0.0123        | 14.0  | 5250  | 0.9022          | 0.885    |
| 0.0631        | 15.0  | 5625  | 0.9459          | 0.875    |
| 0.0113        | 16.0  | 6000  | 0.9699          | 0.8717   |
| 0.0335        | 17.0  | 6375  | 0.9476          | 0.8867   |
| 0.014         | 18.0  | 6750  | 0.9219          | 0.8833   |
| 0.0214        | 19.0  | 7125  | 0.9440          | 0.8833   |
| 0.0134        | 20.0  | 7500  | 0.9965          | 0.8883   |
| 0.0053        | 21.0  | 7875  | 0.9823          | 0.8883   |
| 0.0026        | 22.0  | 8250  | 1.0367          | 0.8817   |
| 0.0174        | 23.0  | 8625  | 1.0171          | 0.8833   |
| 0.0002        | 24.0  | 9000  | 1.0087          | 0.8817   |
| 0.032         | 25.0  | 9375  | 1.0135          | 0.8917   |
| 0.0014        | 26.0  | 9750  | 1.0465          | 0.885    |
| 0.0249        | 27.0  | 10125 | 1.0322          | 0.8833   |
| 0.0001        | 28.0  | 10500 | 1.0170          | 0.8917   |
| 0.0002        | 29.0  | 10875 | 1.0011          | 0.8933   |
| 0.0298        | 30.0  | 11250 | 1.0259          | 0.89     |
| 0.006         | 31.0  | 11625 | 1.0094          | 0.8867   |
| 0.0305        | 32.0  | 12000 | 1.0462          | 0.885    |
| 0.0001        | 33.0  | 12375 | 1.0305          | 0.8917   |
| 0.0011        | 34.0  | 12750 | 1.0273          | 0.89     |
| 0.0001        | 35.0  | 13125 | 1.0726          | 0.89     |
| 0.0022        | 36.0  | 13500 | 1.0573          | 0.8917   |
| 0.0086        | 37.0  | 13875 | 1.0713          | 0.8833   |
| 0.0263        | 38.0  | 14250 | 1.0516          | 0.89     |
| 0.0001        | 39.0  | 14625 | 1.0815          | 0.8867   |
| 0.0002        | 40.0  | 15000 | 1.0626          | 0.8833   |
| 0.0099        | 41.0  | 15375 | 1.0610          | 0.8883   |
| 0.0004        | 42.0  | 15750 | 1.0801          | 0.885    |
| 0.0001        | 43.0  | 16125 | 1.0685          | 0.8867   |
| 0.007         | 44.0  | 16500 | 1.0616          | 0.8967   |
| 0.0           | 45.0  | 16875 | 1.0741          | 0.8917   |
| 0.0172        | 46.0  | 17250 | 1.0800          | 0.8883   |
| 0.004         | 47.0  | 17625 | 1.0888          | 0.89     |
| 0.0113        | 48.0  | 18000 | 1.0832          | 0.8867   |
| 0.0002        | 49.0  | 18375 | 1.0808          | 0.8867   |
| 0.0001        | 50.0  | 18750 | 1.0796          | 0.8883   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-base-patch16-224", "model-index": [{"name": "smids_3x_beit_base_adamax_00001_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-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us