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# SMIDS_10x_beit_large_rmsprop_lr0001_fold3

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: 1.2702
- Accuracy: 0.9083

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2663        | 1.0   | 1500  | 0.3421          | 0.8767   |
| 0.2162        | 2.0   | 3000  | 0.3712          | 0.8633   |
| 0.1789        | 3.0   | 4500  | 0.4013          | 0.8783   |
| 0.1226        | 4.0   | 6000  | 0.4600          | 0.8867   |
| 0.161         | 5.0   | 7500  | 0.5050          | 0.8833   |
| 0.119         | 6.0   | 9000  | 0.4478          | 0.88     |
| 0.0892        | 7.0   | 10500 | 0.6145          | 0.8833   |
| 0.0967        | 8.0   | 12000 | 0.5719          | 0.8967   |
| 0.0049        | 9.0   | 13500 | 0.5646          | 0.8833   |
| 0.0156        | 10.0  | 15000 | 0.7140          | 0.8867   |
| 0.056         | 11.0  | 16500 | 0.6657          | 0.8867   |
| 0.0065        | 12.0  | 18000 | 0.7148          | 0.8883   |
| 0.0099        | 13.0  | 19500 | 0.6783          | 0.8883   |
| 0.042         | 14.0  | 21000 | 0.6376          | 0.8883   |
| 0.0033        | 15.0  | 22500 | 0.7844          | 0.8717   |
| 0.0173        | 16.0  | 24000 | 0.6651          | 0.9017   |
| 0.0003        | 17.0  | 25500 | 0.7821          | 0.875    |
| 0.106         | 18.0  | 27000 | 0.6941          | 0.8933   |
| 0.0015        | 19.0  | 28500 | 0.6536          | 0.9017   |
| 0.0121        | 20.0  | 30000 | 0.7022          | 0.9017   |
| 0.0008        | 21.0  | 31500 | 1.0923          | 0.89     |
| 0.0003        | 22.0  | 33000 | 0.7441          | 0.9033   |
| 0.0023        | 23.0  | 34500 | 0.6691          | 0.9017   |
| 0.0           | 24.0  | 36000 | 0.8871          | 0.895    |
| 0.0002        | 25.0  | 37500 | 0.8785          | 0.8917   |
| 0.0003        | 26.0  | 39000 | 0.8965          | 0.895    |
| 0.0001        | 27.0  | 40500 | 0.7648          | 0.9      |
| 0.0138        | 28.0  | 42000 | 0.9325          | 0.89     |
| 0.0121        | 29.0  | 43500 | 0.8230          | 0.8933   |
| 0.0001        | 30.0  | 45000 | 0.7848          | 0.9033   |
| 0.0           | 31.0  | 46500 | 0.9070          | 0.8983   |
| 0.0           | 32.0  | 48000 | 0.9286          | 0.8967   |
| 0.0529        | 33.0  | 49500 | 1.0225          | 0.89     |
| 0.0           | 34.0  | 51000 | 1.1451          | 0.895    |
| 0.0138        | 35.0  | 52500 | 1.0843          | 0.8983   |
| 0.0           | 36.0  | 54000 | 1.0668          | 0.8917   |
| 0.0           | 37.0  | 55500 | 1.0616          | 0.8933   |
| 0.0           | 38.0  | 57000 | 1.1525          | 0.895    |
| 0.0           | 39.0  | 58500 | 1.0601          | 0.9017   |
| 0.0           | 40.0  | 60000 | 1.2394          | 0.895    |
| 0.0           | 41.0  | 61500 | 1.1614          | 0.8917   |
| 0.0           | 42.0  | 63000 | 1.1805          | 0.8983   |
| 0.0           | 43.0  | 64500 | 1.2289          | 0.895    |
| 0.0           | 44.0  | 66000 | 1.2766          | 0.8967   |
| 0.0           | 45.0  | 67500 | 1.2526          | 0.905    |
| 0.0           | 46.0  | 69000 | 1.3078          | 0.9017   |
| 0.0           | 47.0  | 70500 | 1.2438          | 0.905    |
| 0.0           | 48.0  | 72000 | 1.2669          | 0.9067   |
| 0.0           | 49.0  | 73500 | 1.2684          | 0.9083   |
| 0.0           | 50.0  | 75000 | 1.2702          | 0.9083   |


### 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_10x_beit_large_rmsprop_lr0001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9083333333333333, "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