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# SMIDS_10x_beit_base_sgd_lr00001_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: 0.7642
- Accuracy: 0.6767

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1417        | 1.0   | 750   | 1.0386          | 0.435    |
| 1.0676        | 2.0   | 1500  | 1.0192          | 0.4517   |
| 1.1066        | 3.0   | 2250  | 1.0016          | 0.4717   |
| 1.0727        | 4.0   | 3000  | 0.9853          | 0.4917   |
| 1.0357        | 5.0   | 3750  | 0.9703          | 0.51     |
| 1.0207        | 6.0   | 4500  | 0.9563          | 0.525    |
| 0.9778        | 7.0   | 5250  | 0.9435          | 0.5333   |
| 0.9926        | 8.0   | 6000  | 0.9316          | 0.5517   |
| 0.9927        | 9.0   | 6750  | 0.9207          | 0.5533   |
| 0.9617        | 10.0  | 7500  | 0.9107          | 0.565    |
| 0.9245        | 11.0  | 8250  | 0.9012          | 0.5833   |
| 0.9265        | 12.0  | 9000  | 0.8922          | 0.5967   |
| 0.9381        | 13.0  | 9750  | 0.8837          | 0.605    |
| 0.8952        | 14.0  | 10500 | 0.8757          | 0.6083   |
| 0.873         | 15.0  | 11250 | 0.8679          | 0.6083   |
| 0.9108        | 16.0  | 12000 | 0.8609          | 0.6133   |
| 0.9017        | 17.0  | 12750 | 0.8541          | 0.6233   |
| 0.9015        | 18.0  | 13500 | 0.8476          | 0.6267   |
| 0.8731        | 19.0  | 14250 | 0.8417          | 0.635    |
| 0.8623        | 20.0  | 15000 | 0.8359          | 0.6367   |
| 0.8556        | 21.0  | 15750 | 0.8305          | 0.64     |
| 0.8603        | 22.0  | 16500 | 0.8254          | 0.6433   |
| 0.8486        | 23.0  | 17250 | 0.8204          | 0.65     |
| 0.8757        | 24.0  | 18000 | 0.8159          | 0.6567   |
| 0.8113        | 25.0  | 18750 | 0.8115          | 0.6583   |
| 0.8182        | 26.0  | 19500 | 0.8073          | 0.66     |
| 0.8572        | 27.0  | 20250 | 0.8034          | 0.655    |
| 0.8027        | 28.0  | 21000 | 0.7997          | 0.6583   |
| 0.807         | 29.0  | 21750 | 0.7962          | 0.6617   |
| 0.8073        | 30.0  | 22500 | 0.7929          | 0.6633   |
| 0.8207        | 31.0  | 23250 | 0.7899          | 0.6683   |
| 0.8226        | 32.0  | 24000 | 0.7871          | 0.67     |
| 0.8788        | 33.0  | 24750 | 0.7845          | 0.6717   |
| 0.8079        | 34.0  | 25500 | 0.7820          | 0.6733   |
| 0.831         | 35.0  | 26250 | 0.7797          | 0.6733   |
| 0.7995        | 36.0  | 27000 | 0.7775          | 0.675    |
| 0.7839        | 37.0  | 27750 | 0.7755          | 0.6767   |
| 0.7747        | 38.0  | 28500 | 0.7737          | 0.6783   |
| 0.8512        | 39.0  | 29250 | 0.7720          | 0.6783   |
| 0.816         | 40.0  | 30000 | 0.7706          | 0.6767   |
| 0.8067        | 41.0  | 30750 | 0.7694          | 0.675    |
| 0.7798        | 42.0  | 31500 | 0.7682          | 0.675    |
| 0.809         | 43.0  | 32250 | 0.7673          | 0.675    |
| 0.7708        | 44.0  | 33000 | 0.7664          | 0.675    |
| 0.7687        | 45.0  | 33750 | 0.7657          | 0.6767   |
| 0.8346        | 46.0  | 34500 | 0.7652          | 0.6767   |
| 0.7983        | 47.0  | 35250 | 0.7647          | 0.6767   |
| 0.8165        | 48.0  | 36000 | 0.7644          | 0.6767   |
| 0.7713        | 49.0  | 36750 | 0.7643          | 0.6767   |
| 0.7637        | 50.0  | 37500 | 0.7642          | 0.6767   |


### 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-base-patch16-224", "model-index": [{"name": "SMIDS_10x_beit_base_sgd_lr00001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6766666666666666, "name": "Accuracy"}]}]}]}
transformers safetensors 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