
<!-- 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_5x_beit_large_Adamax_lr00001_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: 0.8201
- Accuracy: 0.925

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1932        | 1.0   | 750   | 0.2436          | 0.9083   |
| 0.1543        | 2.0   | 1500  | 0.2753          | 0.93     |
| 0.0232        | 3.0   | 2250  | 0.3855          | 0.92     |
| 0.1171        | 4.0   | 3000  | 0.3879          | 0.9267   |
| 0.0196        | 5.0   | 3750  | 0.5631          | 0.91     |
| 0.0032        | 6.0   | 4500  | 0.5683          | 0.9333   |
| 0.0           | 7.0   | 5250  | 0.5874          | 0.9183   |
| 0.0           | 8.0   | 6000  | 0.6186          | 0.9283   |
| 0.0014        | 9.0   | 6750  | 0.6549          | 0.9217   |
| 0.0001        | 10.0  | 7500  | 0.6650          | 0.9233   |
| 0.0001        | 11.0  | 8250  | 0.6879          | 0.9233   |
| 0.0483        | 12.0  | 9000  | 0.6811          | 0.9283   |
| 0.0009        | 13.0  | 9750  | 0.7081          | 0.9233   |
| 0.0           | 14.0  | 10500 | 0.7026          | 0.9217   |
| 0.0189        | 15.0  | 11250 | 0.7065          | 0.9183   |
| 0.0           | 16.0  | 12000 | 0.7912          | 0.925    |
| 0.0           | 17.0  | 12750 | 0.7658          | 0.9217   |
| 0.0           | 18.0  | 13500 | 0.7715          | 0.9267   |
| 0.0033        | 19.0  | 14250 | 0.8350          | 0.9183   |
| 0.0002        | 20.0  | 15000 | 0.7615          | 0.9233   |
| 0.0           | 21.0  | 15750 | 0.7455          | 0.93     |
| 0.0           | 22.0  | 16500 | 0.7486          | 0.9283   |
| 0.0           | 23.0  | 17250 | 0.7927          | 0.9283   |
| 0.0           | 24.0  | 18000 | 0.7499          | 0.925    |
| 0.0           | 25.0  | 18750 | 0.8271          | 0.92     |
| 0.0           | 26.0  | 19500 | 0.7775          | 0.925    |
| 0.0           | 27.0  | 20250 | 0.7598          | 0.9283   |
| 0.0           | 28.0  | 21000 | 0.8126          | 0.9283   |
| 0.0           | 29.0  | 21750 | 0.7591          | 0.9283   |
| 0.0           | 30.0  | 22500 | 0.8273          | 0.925    |
| 0.0           | 31.0  | 23250 | 0.8612          | 0.92     |
| 0.0001        | 32.0  | 24000 | 0.7764          | 0.9233   |
| 0.0001        | 33.0  | 24750 | 0.8359          | 0.9183   |
| 0.0           | 34.0  | 25500 | 0.8263          | 0.9217   |
| 0.0           | 35.0  | 26250 | 0.8373          | 0.9233   |
| 0.0259        | 36.0  | 27000 | 0.8318          | 0.9233   |
| 0.0           | 37.0  | 27750 | 0.8061          | 0.9233   |
| 0.0           | 38.0  | 28500 | 0.8070          | 0.925    |
| 0.0           | 39.0  | 29250 | 0.7972          | 0.9233   |
| 0.0001        | 40.0  | 30000 | 0.7694          | 0.925    |
| 0.0           | 41.0  | 30750 | 0.7918          | 0.9233   |
| 0.0           | 42.0  | 31500 | 0.8097          | 0.9267   |
| 0.0           | 43.0  | 32250 | 0.8241          | 0.9283   |
| 0.0           | 44.0  | 33000 | 0.8229          | 0.9267   |
| 0.0           | 45.0  | 33750 | 0.8175          | 0.925    |
| 0.0           | 46.0  | 34500 | 0.8270          | 0.9233   |
| 0.0           | 47.0  | 35250 | 0.8251          | 0.925    |
| 0.0           | 48.0  | 36000 | 0.8185          | 0.9217   |
| 0.0           | 49.0  | 36750 | 0.8199          | 0.925    |
| 0.0           | 50.0  | 37500 | 0.8201          | 0.925    |


### 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_5x_beit_large_Adamax_lr00001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.925, "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