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# SMIDS_10x_beit_base_sgd_lr0001_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: 0.3762
- Accuracy: 0.855

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9601        | 1.0   | 750   | 0.8925          | 0.5933   |
| 0.814         | 2.0   | 1500  | 0.7888          | 0.6667   |
| 0.7602        | 3.0   | 2250  | 0.7147          | 0.7267   |
| 0.7232        | 4.0   | 3000  | 0.6605          | 0.7467   |
| 0.6593        | 5.0   | 3750  | 0.6190          | 0.7617   |
| 0.5788        | 6.0   | 4500  | 0.5863          | 0.7817   |
| 0.5359        | 7.0   | 5250  | 0.5585          | 0.79     |
| 0.5232        | 8.0   | 6000  | 0.5356          | 0.805    |
| 0.4971        | 9.0   | 6750  | 0.5156          | 0.8083   |
| 0.5006        | 10.0  | 7500  | 0.4986          | 0.8083   |
| 0.4259        | 11.0  | 8250  | 0.4843          | 0.8133   |
| 0.4225        | 12.0  | 9000  | 0.4716          | 0.8217   |
| 0.4201        | 13.0  | 9750  | 0.4606          | 0.83     |
| 0.4002        | 14.0  | 10500 | 0.4507          | 0.8383   |
| 0.4026        | 15.0  | 11250 | 0.4423          | 0.835    |
| 0.4305        | 16.0  | 12000 | 0.4346          | 0.8333   |
| 0.3838        | 17.0  | 12750 | 0.4284          | 0.8417   |
| 0.4015        | 18.0  | 13500 | 0.4226          | 0.8433   |
| 0.3889        | 19.0  | 14250 | 0.4188          | 0.8433   |
| 0.3422        | 20.0  | 15000 | 0.4135          | 0.8433   |
| 0.3884        | 21.0  | 15750 | 0.4095          | 0.8467   |
| 0.3577        | 22.0  | 16500 | 0.4063          | 0.845    |
| 0.3323        | 23.0  | 17250 | 0.4027          | 0.8483   |
| 0.4119        | 24.0  | 18000 | 0.3994          | 0.85     |
| 0.3475        | 25.0  | 18750 | 0.3969          | 0.8483   |
| 0.3603        | 26.0  | 19500 | 0.3947          | 0.8467   |
| 0.3564        | 27.0  | 20250 | 0.3928          | 0.8467   |
| 0.311         | 28.0  | 21000 | 0.3906          | 0.8467   |
| 0.3463        | 29.0  | 21750 | 0.3887          | 0.8517   |
| 0.3545        | 30.0  | 22500 | 0.3871          | 0.85     |
| 0.3366        | 31.0  | 23250 | 0.3859          | 0.85     |
| 0.3467        | 32.0  | 24000 | 0.3854          | 0.85     |
| 0.3903        | 33.0  | 24750 | 0.3843          | 0.8483   |
| 0.3172        | 34.0  | 25500 | 0.3830          | 0.85     |
| 0.3307        | 35.0  | 26250 | 0.3821          | 0.8483   |
| 0.3261        | 36.0  | 27000 | 0.3809          | 0.85     |
| 0.3257        | 37.0  | 27750 | 0.3802          | 0.8517   |
| 0.3496        | 38.0  | 28500 | 0.3796          | 0.85     |
| 0.3354        | 39.0  | 29250 | 0.3789          | 0.85     |
| 0.3729        | 40.0  | 30000 | 0.3783          | 0.855    |
| 0.3449        | 41.0  | 30750 | 0.3779          | 0.8533   |
| 0.3081        | 42.0  | 31500 | 0.3776          | 0.8533   |
| 0.3178        | 43.0  | 32250 | 0.3771          | 0.855    |
| 0.3304        | 44.0  | 33000 | 0.3770          | 0.855    |
| 0.2831        | 45.0  | 33750 | 0.3766          | 0.8533   |
| 0.3455        | 46.0  | 34500 | 0.3764          | 0.855    |
| 0.3298        | 47.0  | 35250 | 0.3763          | 0.855    |
| 0.3519        | 48.0  | 36000 | 0.3762          | 0.855    |
| 0.2868        | 49.0  | 36750 | 0.3762          | 0.855    |
| 0.3311        | 50.0  | 37500 | 0.3762          | 0.855    |


### 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_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.855, "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