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# SMIDS_10x_beit_base_sgd_lr001_fold2

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: 0.2662
- Accuracy: 0.8952

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5323        | 1.0   | 750   | 0.5389          | 0.7770   |
| 0.3411        | 2.0   | 1500  | 0.4223          | 0.8220   |
| 0.3559        | 3.0   | 2250  | 0.3767          | 0.8336   |
| 0.4128        | 4.0   | 3000  | 0.3562          | 0.8403   |
| 0.3172        | 5.0   | 3750  | 0.3502          | 0.8502   |
| 0.3206        | 6.0   | 4500  | 0.3276          | 0.8519   |
| 0.2677        | 7.0   | 5250  | 0.3157          | 0.8686   |
| 0.268         | 8.0   | 6000  | 0.2951          | 0.8785   |
| 0.2969        | 9.0   | 6750  | 0.2899          | 0.8802   |
| 0.2771        | 10.0  | 7500  | 0.2958          | 0.8686   |
| 0.2786        | 11.0  | 8250  | 0.2815          | 0.8785   |
| 0.2876        | 12.0  | 9000  | 0.2797          | 0.8752   |
| 0.2472        | 13.0  | 9750  | 0.2836          | 0.8785   |
| 0.18          | 14.0  | 10500 | 0.2738          | 0.8769   |
| 0.185         | 15.0  | 11250 | 0.2730          | 0.8752   |
| 0.2107        | 16.0  | 12000 | 0.2727          | 0.8769   |
| 0.1852        | 17.0  | 12750 | 0.2730          | 0.8869   |
| 0.1934        | 18.0  | 13500 | 0.2741          | 0.8785   |
| 0.2275        | 19.0  | 14250 | 0.2756          | 0.8785   |
| 0.2226        | 20.0  | 15000 | 0.2710          | 0.8852   |
| 0.2055        | 21.0  | 15750 | 0.2635          | 0.8885   |
| 0.1662        | 22.0  | 16500 | 0.2617          | 0.8918   |
| 0.1926        | 23.0  | 17250 | 0.2652          | 0.8802   |
| 0.1642        | 24.0  | 18000 | 0.2623          | 0.8952   |
| 0.206         | 25.0  | 18750 | 0.2639          | 0.8935   |
| 0.2319        | 26.0  | 19500 | 0.2745          | 0.8885   |
| 0.2263        | 27.0  | 20250 | 0.2677          | 0.8819   |
| 0.1806        | 28.0  | 21000 | 0.2655          | 0.8885   |
| 0.2562        | 29.0  | 21750 | 0.2668          | 0.8902   |
| 0.1677        | 30.0  | 22500 | 0.2656          | 0.8935   |
| 0.1963        | 31.0  | 23250 | 0.2657          | 0.8918   |
| 0.2223        | 32.0  | 24000 | 0.2643          | 0.8869   |
| 0.1923        | 33.0  | 24750 | 0.2609          | 0.9002   |
| 0.2037        | 34.0  | 25500 | 0.2629          | 0.8918   |
| 0.216         | 35.0  | 26250 | 0.2631          | 0.8935   |
| 0.1958        | 36.0  | 27000 | 0.2718          | 0.8885   |
| 0.1706        | 37.0  | 27750 | 0.2711          | 0.8885   |
| 0.1746        | 38.0  | 28500 | 0.2679          | 0.8935   |
| 0.1483        | 39.0  | 29250 | 0.2684          | 0.8918   |
| 0.2674        | 40.0  | 30000 | 0.2679          | 0.8869   |
| 0.1919        | 41.0  | 30750 | 0.2677          | 0.8918   |
| 0.1702        | 42.0  | 31500 | 0.2680          | 0.8935   |
| 0.1648        | 43.0  | 32250 | 0.2680          | 0.8902   |
| 0.1342        | 44.0  | 33000 | 0.2644          | 0.8968   |
| 0.1926        | 45.0  | 33750 | 0.2684          | 0.8985   |
| 0.2134        | 46.0  | 34500 | 0.2656          | 0.8935   |
| 0.2002        | 47.0  | 35250 | 0.2669          | 0.8935   |
| 0.1452        | 48.0  | 36000 | 0.2648          | 0.8952   |
| 0.1532        | 49.0  | 36750 | 0.2667          | 0.8935   |
| 0.1252        | 50.0  | 37500 | 0.2662          | 0.8952   |


### 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_lr001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8951747088186356, "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