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# SMIDS_10x_beit_large_adamax_lr001_fold4

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: 2.2002
- Accuracy: 0.8333

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6405        | 1.0   | 1500  | 0.5883          | 0.765    |
| 0.642         | 2.0   | 3000  | 0.5075          | 0.8083   |
| 0.5476        | 3.0   | 4500  | 0.5336          | 0.8233   |
| 0.428         | 4.0   | 6000  | 0.5026          | 0.8133   |
| 0.3733        | 5.0   | 7500  | 0.6401          | 0.79     |
| 0.273         | 6.0   | 9000  | 0.5549          | 0.8167   |
| 0.1991        | 7.0   | 10500 | 0.5297          | 0.8133   |
| 0.3551        | 8.0   | 12000 | 0.6009          | 0.8167   |
| 0.1958        | 9.0   | 13500 | 0.5953          | 0.8167   |
| 0.2541        | 10.0  | 15000 | 0.6735          | 0.8233   |
| 0.2477        | 11.0  | 16500 | 0.7204          | 0.7983   |
| 0.2187        | 12.0  | 18000 | 0.6387          | 0.84     |
| 0.192         | 13.0  | 19500 | 0.8130          | 0.8317   |
| 0.162         | 14.0  | 21000 | 0.9305          | 0.8183   |
| 0.3023        | 15.0  | 22500 | 0.8213          | 0.8183   |
| 0.1453        | 16.0  | 24000 | 1.0962          | 0.82     |
| 0.0813        | 17.0  | 25500 | 1.0126          | 0.82     |
| 0.1297        | 18.0  | 27000 | 1.0537          | 0.8183   |
| 0.0319        | 19.0  | 28500 | 1.0515          | 0.8467   |
| 0.0964        | 20.0  | 30000 | 1.0492          | 0.83     |
| 0.0392        | 21.0  | 31500 | 1.2224          | 0.83     |
| 0.0348        | 22.0  | 33000 | 1.3260          | 0.8217   |
| 0.0055        | 23.0  | 34500 | 1.1763          | 0.84     |
| 0.0745        | 24.0  | 36000 | 1.3159          | 0.8383   |
| 0.0282        | 25.0  | 37500 | 1.4495          | 0.82     |
| 0.0057        | 26.0  | 39000 | 1.6141          | 0.835    |
| 0.0003        | 27.0  | 40500 | 1.5153          | 0.8183   |
| 0.0671        | 28.0  | 42000 | 1.3271          | 0.8283   |
| 0.015         | 29.0  | 43500 | 1.5099          | 0.8317   |
| 0.0004        | 30.0  | 45000 | 1.4830          | 0.84     |
| 0.0001        | 31.0  | 46500 | 1.7034          | 0.825    |
| 0.0768        | 32.0  | 48000 | 1.6997          | 0.825    |
| 0.0161        | 33.0  | 49500 | 1.6698          | 0.82     |
| 0.0002        | 34.0  | 51000 | 2.1445          | 0.8133   |
| 0.0002        | 35.0  | 52500 | 2.1620          | 0.815    |
| 0.0005        | 36.0  | 54000 | 2.1925          | 0.8267   |
| 0.0001        | 37.0  | 55500 | 1.8218          | 0.825    |
| 0.0002        | 38.0  | 57000 | 1.8525          | 0.83     |
| 0.0002        | 39.0  | 58500 | 1.7934          | 0.83     |
| 0.0002        | 40.0  | 60000 | 2.0896          | 0.8317   |
| 0.0001        | 41.0  | 61500 | 2.0195          | 0.8367   |
| 0.0           | 42.0  | 63000 | 2.4880          | 0.8267   |
| 0.014         | 43.0  | 64500 | 2.3846          | 0.8233   |
| 0.0733        | 44.0  | 66000 | 2.2030          | 0.835    |
| 0.0           | 45.0  | 67500 | 2.2024          | 0.8317   |
| 0.0           | 46.0  | 69000 | 1.9829          | 0.84     |
| 0.0001        | 47.0  | 70500 | 2.0646          | 0.8417   |
| 0.0           | 48.0  | 72000 | 2.2099          | 0.8367   |
| 0.0154        | 49.0  | 73500 | 2.2123          | 0.8333   |
| 0.0           | 50.0  | 75000 | 2.2002          | 0.8333   |


### 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_adamax_lr001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8333333333333334, "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