
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# SMIDS_1x_beit_large_SGD_lr0001_fold1

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: 0.5247
- Accuracy: 0.8013

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0713        | 1.0   | 151  | 1.1483          | 0.4190   |
| 1.0438        | 2.0   | 302  | 1.0414          | 0.4741   |
| 0.985         | 3.0   | 453  | 0.9774          | 0.5476   |
| 0.9773        | 4.0   | 604  | 0.9305          | 0.5810   |
| 0.9269        | 5.0   | 755  | 0.8908          | 0.6144   |
| 0.8348        | 6.0   | 906  | 0.8569          | 0.6327   |
| 0.8371        | 7.0   | 1057 | 0.8264          | 0.6644   |
| 0.7847        | 8.0   | 1208 | 0.7992          | 0.6761   |
| 0.7583        | 9.0   | 1359 | 0.7749          | 0.6962   |
| 0.849         | 10.0  | 1510 | 0.7533          | 0.7112   |
| 0.688         | 11.0  | 1661 | 0.7331          | 0.7262   |
| 0.7639        | 12.0  | 1812 | 0.7150          | 0.7346   |
| 0.6996        | 13.0  | 1963 | 0.6986          | 0.7446   |
| 0.6872        | 14.0  | 2114 | 0.6835          | 0.7429   |
| 0.6847        | 15.0  | 2265 | 0.6696          | 0.7446   |
| 0.6494        | 16.0  | 2416 | 0.6571          | 0.7513   |
| 0.6751        | 17.0  | 2567 | 0.6453          | 0.7563   |
| 0.5983        | 18.0  | 2718 | 0.6354          | 0.7629   |
| 0.6374        | 19.0  | 2869 | 0.6259          | 0.7713   |
| 0.5663        | 20.0  | 3020 | 0.6172          | 0.7746   |
| 0.6944        | 21.0  | 3171 | 0.6093          | 0.7763   |
| 0.6221        | 22.0  | 3322 | 0.6018          | 0.7780   |
| 0.5646        | 23.0  | 3473 | 0.5950          | 0.7763   |
| 0.5817        | 24.0  | 3624 | 0.5887          | 0.7813   |
| 0.5511        | 25.0  | 3775 | 0.5829          | 0.7846   |
| 0.5743        | 26.0  | 3926 | 0.5775          | 0.7863   |
| 0.5302        | 27.0  | 4077 | 0.5723          | 0.7846   |
| 0.5742        | 28.0  | 4228 | 0.5674          | 0.7863   |
| 0.6139        | 29.0  | 4379 | 0.5627          | 0.7896   |
| 0.5041        | 30.0  | 4530 | 0.5586          | 0.7930   |
| 0.5239        | 31.0  | 4681 | 0.5547          | 0.7930   |
| 0.5679        | 32.0  | 4832 | 0.5512          | 0.7947   |
| 0.5271        | 33.0  | 4983 | 0.5480          | 0.7963   |
| 0.5175        | 34.0  | 5134 | 0.5451          | 0.7963   |
| 0.5558        | 35.0  | 5285 | 0.5423          | 0.7980   |
| 0.5591        | 36.0  | 5436 | 0.5399          | 0.7980   |
| 0.4721        | 37.0  | 5587 | 0.5377          | 0.7980   |
| 0.5598        | 38.0  | 5738 | 0.5355          | 0.7980   |
| 0.5497        | 39.0  | 5889 | 0.5337          | 0.7980   |
| 0.508         | 40.0  | 6040 | 0.5320          | 0.7980   |
| 0.5882        | 41.0  | 6191 | 0.5305          | 0.7997   |
| 0.5313        | 42.0  | 6342 | 0.5291          | 0.7997   |
| 0.4577        | 43.0  | 6493 | 0.5279          | 0.7997   |
| 0.5031        | 44.0  | 6644 | 0.5270          | 0.8013   |
| 0.5734        | 45.0  | 6795 | 0.5262          | 0.8013   |
| 0.5499        | 46.0  | 6946 | 0.5256          | 0.8013   |
| 0.5386        | 47.0  | 7097 | 0.5252          | 0.8013   |
| 0.454         | 48.0  | 7248 | 0.5249          | 0.8013   |
| 0.5559        | 49.0  | 7399 | 0.5247          | 0.8013   |
| 0.5764        | 50.0  | 7550 | 0.5247          | 0.8013   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2

{"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_lr0001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8013355592654424, "name": "Accuracy"}]}]}]}
transformers pytorch 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