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# SMIDS_5x_beit_large_Adamax_lr001_fold2

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: 1.3248
- Accuracy: 0.8852

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6352        | 1.0   | 750   | 0.6175          | 0.7304   |
| 0.4813        | 2.0   | 1500  | 0.4032          | 0.8369   |
| 0.3595        | 3.0   | 2250  | 0.3744          | 0.8419   |
| 0.3483        | 4.0   | 3000  | 0.3898          | 0.8569   |
| 0.2404        | 5.0   | 3750  | 0.3033          | 0.8852   |
| 0.2424        | 6.0   | 4500  | 0.3882          | 0.8602   |
| 0.2135        | 7.0   | 5250  | 0.4091          | 0.8669   |
| 0.1817        | 8.0   | 6000  | 0.3604          | 0.8769   |
| 0.1333        | 9.0   | 6750  | 0.4843          | 0.8536   |
| 0.071         | 10.0  | 7500  | 0.4051          | 0.8785   |
| 0.0744        | 11.0  | 8250  | 0.4547          | 0.8719   |
| 0.095         | 12.0  | 9000  | 0.6509          | 0.8636   |
| 0.1175        | 13.0  | 9750  | 0.5492          | 0.8902   |
| 0.082         | 14.0  | 10500 | 0.5438          | 0.8752   |
| 0.0529        | 15.0  | 11250 | 0.5695          | 0.8669   |
| 0.0494        | 16.0  | 12000 | 0.5774          | 0.9018   |
| 0.0492        | 17.0  | 12750 | 0.7428          | 0.8735   |
| 0.0359        | 18.0  | 13500 | 0.6694          | 0.8869   |
| 0.0627        | 19.0  | 14250 | 0.6717          | 0.8835   |
| 0.0443        | 20.0  | 15000 | 0.8233          | 0.8885   |
| 0.0021        | 21.0  | 15750 | 0.7305          | 0.8819   |
| 0.004         | 22.0  | 16500 | 0.8970          | 0.8852   |
| 0.0395        | 23.0  | 17250 | 0.7750          | 0.8719   |
| 0.0182        | 24.0  | 18000 | 0.7781          | 0.8985   |
| 0.0776        | 25.0  | 18750 | 0.6486          | 0.9002   |
| 0.0005        | 26.0  | 19500 | 0.7753          | 0.8935   |
| 0.035         | 27.0  | 20250 | 0.9573          | 0.8769   |
| 0.0001        | 28.0  | 21000 | 0.8967          | 0.8852   |
| 0.0004        | 29.0  | 21750 | 0.9020          | 0.8785   |
| 0.0173        | 30.0  | 22500 | 1.0139          | 0.8769   |
| 0.0002        | 31.0  | 23250 | 0.7762          | 0.8835   |
| 0.0           | 32.0  | 24000 | 0.9461          | 0.8869   |
| 0.0007        | 33.0  | 24750 | 1.0527          | 0.8785   |
| 0.0345        | 34.0  | 25500 | 0.9816          | 0.8869   |
| 0.0193        | 35.0  | 26250 | 0.9973          | 0.8852   |
| 0.0           | 36.0  | 27000 | 1.0679          | 0.8968   |
| 0.0078        | 37.0  | 27750 | 1.1029          | 0.8869   |
| 0.0           | 38.0  | 28500 | 1.1632          | 0.8902   |
| 0.0           | 39.0  | 29250 | 1.1956          | 0.8902   |
| 0.0           | 40.0  | 30000 | 1.1094          | 0.8902   |
| 0.0           | 41.0  | 30750 | 1.2380          | 0.8819   |
| 0.0           | 42.0  | 31500 | 1.2850          | 0.8869   |
| 0.0           | 43.0  | 32250 | 1.3008          | 0.8835   |
| 0.0003        | 44.0  | 33000 | 1.3024          | 0.8835   |
| 0.0           | 45.0  | 33750 | 1.2608          | 0.8835   |
| 0.0           | 46.0  | 34500 | 1.2526          | 0.8852   |
| 0.0           | 47.0  | 35250 | 1.2944          | 0.8869   |
| 0.0           | 48.0  | 36000 | 1.3137          | 0.8869   |
| 0.0           | 49.0  | 36750 | 1.3225          | 0.8852   |
| 0.0           | 50.0  | 37500 | 1.3248          | 0.8852   |


### 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_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": 0.8851913477537438, "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