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# SMIDS_1x_beit_large_SGD_lr001_fold5

This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2829
- Accuracy: 0.8817

## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8178        | 1.0   | 150  | 0.7962          | 0.6567   |
| 0.5931        | 2.0   | 300  | 0.6027          | 0.7717   |
| 0.4806        | 3.0   | 450  | 0.5037          | 0.81     |
| 0.487         | 4.0   | 600  | 0.4433          | 0.8217   |
| 0.455         | 5.0   | 750  | 0.4037          | 0.845    |
| 0.3519        | 6.0   | 900  | 0.3777          | 0.8467   |
| 0.3658        | 7.0   | 1050 | 0.3584          | 0.855    |
| 0.3513        | 8.0   | 1200 | 0.3451          | 0.8567   |
| 0.3053        | 9.0   | 1350 | 0.3330          | 0.8533   |
| 0.3467        | 10.0  | 1500 | 0.3249          | 0.8633   |
| 0.3969        | 11.0  | 1650 | 0.3184          | 0.865    |
| 0.2784        | 12.0  | 1800 | 0.3130          | 0.8717   |
| 0.263         | 13.0  | 1950 | 0.3066          | 0.8783   |
| 0.2219        | 14.0  | 2100 | 0.2988          | 0.8717   |
| 0.2258        | 15.0  | 2250 | 0.2953          | 0.8767   |
| 0.2537        | 16.0  | 2400 | 0.3012          | 0.88     |
| 0.2435        | 17.0  | 2550 | 0.2924          | 0.8783   |
| 0.2791        | 18.0  | 2700 | 0.2959          | 0.8817   |
| 0.2494        | 19.0  | 2850 | 0.2930          | 0.8783   |
| 0.2239        | 20.0  | 3000 | 0.2895          | 0.8767   |
| 0.2254        | 21.0  | 3150 | 0.2892          | 0.8817   |
| 0.3602        | 22.0  | 3300 | 0.2881          | 0.88     |
| 0.2059        | 23.0  | 3450 | 0.2845          | 0.885    |
| 0.1996        | 24.0  | 3600 | 0.2864          | 0.8833   |
| 0.2062        | 25.0  | 3750 | 0.2852          | 0.8783   |
| 0.191         | 26.0  | 3900 | 0.2906          | 0.8833   |
| 0.265         | 27.0  | 4050 | 0.2841          | 0.88     |
| 0.2249        | 28.0  | 4200 | 0.2831          | 0.8833   |
| 0.2475        | 29.0  | 4350 | 0.2847          | 0.88     |
| 0.2311        | 30.0  | 4500 | 0.2813          | 0.8833   |
| 0.171         | 31.0  | 4650 | 0.2776          | 0.89     |
| 0.1828        | 32.0  | 4800 | 0.2797          | 0.8817   |
| 0.2413        | 33.0  | 4950 | 0.2800          | 0.8833   |
| 0.1694        | 34.0  | 5100 | 0.2797          | 0.8833   |
| 0.1988        | 35.0  | 5250 | 0.2810          | 0.8833   |
| 0.2266        | 36.0  | 5400 | 0.2824          | 0.8833   |
| 0.2312        | 37.0  | 5550 | 0.2822          | 0.88     |
| 0.1663        | 38.0  | 5700 | 0.2818          | 0.8817   |
| 0.1996        | 39.0  | 5850 | 0.2815          | 0.8833   |
| 0.1721        | 40.0  | 6000 | 0.2826          | 0.8817   |
| 0.1874        | 41.0  | 6150 | 0.2835          | 0.8833   |
| 0.2593        | 42.0  | 6300 | 0.2817          | 0.8833   |
| 0.2673        | 43.0  | 6450 | 0.2829          | 0.8817   |
| 0.1472        | 44.0  | 6600 | 0.2822          | 0.8817   |
| 0.261         | 45.0  | 6750 | 0.2817          | 0.8817   |
| 0.23          | 46.0  | 6900 | 0.2821          | 0.8817   |
| 0.1917        | 47.0  | 7050 | 0.2833          | 0.8833   |
| 0.1932        | 48.0  | 7200 | 0.2829          | 0.8817   |
| 0.2183        | 49.0  | 7350 | 0.2829          | 0.8817   |
| 0.1952        | 50.0  | 7500 | 0.2829          | 0.8817   |


### 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-large-patch16-224", "model-index": [{"name": "SMIDS_1x_beit_large_SGD_lr001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8816666666666667, "name": "Accuracy"}]}]}]}
transformers safetensors beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-large-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us