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# SMIDS_10x_beit_base_sgd_lr0001_fold5

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: 0.3425
- Accuracy: 0.8567

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9771        | 1.0   | 750   | 0.9045          | 0.555    |
| 0.774         | 2.0   | 1500  | 0.8020          | 0.6433   |
| 0.7242        | 3.0   | 2250  | 0.7309          | 0.685    |
| 0.6881        | 4.0   | 3000  | 0.6766          | 0.7233   |
| 0.6335        | 5.0   | 3750  | 0.6328          | 0.7467   |
| 0.5625        | 6.0   | 4500  | 0.5966          | 0.76     |
| 0.5545        | 7.0   | 5250  | 0.5656          | 0.7767   |
| 0.5577        | 8.0   | 6000  | 0.5406          | 0.7783   |
| 0.4825        | 9.0   | 6750  | 0.5178          | 0.7883   |
| 0.4868        | 10.0  | 7500  | 0.5010          | 0.8017   |
| 0.4765        | 11.0  | 8250  | 0.4843          | 0.805    |
| 0.3781        | 12.0  | 9000  | 0.4689          | 0.815    |
| 0.4649        | 13.0  | 9750  | 0.4565          | 0.82     |
| 0.4075        | 14.0  | 10500 | 0.4457          | 0.8217   |
| 0.4352        | 15.0  | 11250 | 0.4362          | 0.825    |
| 0.4303        | 16.0  | 12000 | 0.4265          | 0.8217   |
| 0.4161        | 17.0  | 12750 | 0.4186          | 0.825    |
| 0.3782        | 18.0  | 13500 | 0.4113          | 0.8317   |
| 0.3823        | 19.0  | 14250 | 0.4060          | 0.8283   |
| 0.3905        | 20.0  | 15000 | 0.3998          | 0.8333   |
| 0.368         | 21.0  | 15750 | 0.3943          | 0.8333   |
| 0.3543        | 22.0  | 16500 | 0.3891          | 0.835    |
| 0.3906        | 23.0  | 17250 | 0.3846          | 0.835    |
| 0.3715        | 24.0  | 18000 | 0.3804          | 0.84     |
| 0.3731        | 25.0  | 18750 | 0.3761          | 0.8417   |
| 0.3693        | 26.0  | 19500 | 0.3728          | 0.8417   |
| 0.3905        | 27.0  | 20250 | 0.3691          | 0.845    |
| 0.354         | 28.0  | 21000 | 0.3666          | 0.8517   |
| 0.3191        | 29.0  | 21750 | 0.3634          | 0.8517   |
| 0.3815        | 30.0  | 22500 | 0.3613          | 0.8517   |
| 0.3795        | 31.0  | 23250 | 0.3592          | 0.8533   |
| 0.3138        | 32.0  | 24000 | 0.3571          | 0.8517   |
| 0.3385        | 33.0  | 24750 | 0.3547          | 0.85     |
| 0.3591        | 34.0  | 25500 | 0.3526          | 0.8517   |
| 0.3654        | 35.0  | 26250 | 0.3514          | 0.855    |
| 0.3478        | 36.0  | 27000 | 0.3501          | 0.855    |
| 0.3987        | 37.0  | 27750 | 0.3488          | 0.855    |
| 0.3382        | 38.0  | 28500 | 0.3477          | 0.855    |
| 0.3006        | 39.0  | 29250 | 0.3468          | 0.855    |
| 0.3594        | 40.0  | 30000 | 0.3459          | 0.8567   |
| 0.3326        | 41.0  | 30750 | 0.3453          | 0.8583   |
| 0.2849        | 42.0  | 31500 | 0.3451          | 0.8567   |
| 0.3533        | 43.0  | 32250 | 0.3446          | 0.8567   |
| 0.3616        | 44.0  | 33000 | 0.3440          | 0.8567   |
| 0.3225        | 45.0  | 33750 | 0.3434          | 0.8583   |
| 0.3696        | 46.0  | 34500 | 0.3433          | 0.8567   |
| 0.3551        | 47.0  | 35250 | 0.3429          | 0.8567   |
| 0.3329        | 48.0  | 36000 | 0.3427          | 0.8567   |
| 0.3161        | 49.0  | 36750 | 0.3425          | 0.8567   |
| 0.3513        | 50.0  | 37500 | 0.3425          | 0.8567   |


### 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_lr0001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8566666666666667, "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