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# smids_1x_beit_base_rms_00001_fold1

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: 0.7081
- Accuracy: 0.8965

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3415        | 1.0   | 76   | 0.3600          | 0.8531   |
| 0.1821        | 2.0   | 152  | 0.2813          | 0.8865   |
| 0.1106        | 3.0   | 228  | 0.2915          | 0.8965   |
| 0.0837        | 4.0   | 304  | 0.4355          | 0.8748   |
| 0.0461        | 5.0   | 380  | 0.3524          | 0.8831   |
| 0.0314        | 6.0   | 456  | 0.3471          | 0.9065   |
| 0.052         | 7.0   | 532  | 0.3906          | 0.9032   |
| 0.0094        | 8.0   | 608  | 0.4902          | 0.8998   |
| 0.0397        | 9.0   | 684  | 0.5074          | 0.8848   |
| 0.0068        | 10.0  | 760  | 0.5396          | 0.8965   |
| 0.0009        | 11.0  | 836  | 0.4910          | 0.9032   |
| 0.0007        | 12.0  | 912  | 0.5441          | 0.8982   |
| 0.0176        | 13.0  | 988  | 0.5729          | 0.8965   |
| 0.008         | 14.0  | 1064 | 0.5831          | 0.8965   |
| 0.0023        | 15.0  | 1140 | 0.6581          | 0.8982   |
| 0.0112        | 16.0  | 1216 | 0.6373          | 0.9048   |
| 0.0122        | 17.0  | 1292 | 0.6091          | 0.8982   |
| 0.0218        | 18.0  | 1368 | 0.7005          | 0.8965   |
| 0.0052        | 19.0  | 1444 | 0.6533          | 0.8998   |
| 0.0143        | 20.0  | 1520 | 0.5987          | 0.9048   |
| 0.0047        | 21.0  | 1596 | 0.6407          | 0.8982   |
| 0.005         | 22.0  | 1672 | 0.7577          | 0.8898   |
| 0.0133        | 23.0  | 1748 | 0.7568          | 0.8848   |
| 0.0064        | 24.0  | 1824 | 0.6963          | 0.8915   |
| 0.0056        | 25.0  | 1900 | 0.6832          | 0.8982   |
| 0.0033        | 26.0  | 1976 | 0.6578          | 0.8982   |
| 0.0048        | 27.0  | 2052 | 0.6821          | 0.9032   |
| 0.0003        | 28.0  | 2128 | 0.6751          | 0.8998   |
| 0.0002        | 29.0  | 2204 | 0.6826          | 0.8998   |
| 0.0054        | 30.0  | 2280 | 0.7208          | 0.8965   |
| 0.0234        | 31.0  | 2356 | 0.7169          | 0.8915   |
| 0.0066        | 32.0  | 2432 | 0.7161          | 0.8982   |
| 0.0078        | 33.0  | 2508 | 0.6895          | 0.8982   |
| 0.004         | 34.0  | 2584 | 0.7616          | 0.8982   |
| 0.0117        | 35.0  | 2660 | 0.7211          | 0.9032   |
| 0.0           | 36.0  | 2736 | 0.6772          | 0.8982   |
| 0.0027        | 37.0  | 2812 | 0.6751          | 0.8998   |
| 0.0023        | 38.0  | 2888 | 0.7465          | 0.9082   |
| 0.0025        | 39.0  | 2964 | 0.6434          | 0.9132   |
| 0.0043        | 40.0  | 3040 | 0.6803          | 0.9032   |
| 0.005         | 41.0  | 3116 | 0.6970          | 0.8982   |
| 0.0           | 42.0  | 3192 | 0.6953          | 0.8998   |
| 0.0002        | 43.0  | 3268 | 0.6864          | 0.8982   |
| 0.0001        | 44.0  | 3344 | 0.6955          | 0.9015   |
| 0.0058        | 45.0  | 3420 | 0.7259          | 0.8948   |
| 0.0           | 46.0  | 3496 | 0.7126          | 0.9032   |
| 0.0044        | 47.0  | 3572 | 0.7081          | 0.8965   |
| 0.0032        | 48.0  | 3648 | 0.7104          | 0.8965   |
| 0.0023        | 49.0  | 3724 | 0.7077          | 0.8965   |
| 0.0057        | 50.0  | 3800 | 0.7081          | 0.8965   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-base-patch16-224", "model-index": [{"name": "smids_1x_beit_base_rms_00001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8964941569282137, "name": "Accuracy"}]}]}]}
transformers tensorboard 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