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# smids_1x_beit_base_adamax_00001_fold2

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7289
- 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4217        | 1.0   | 75   | 0.3814          | 0.8419   |
| 0.2549        | 2.0   | 150  | 0.3222          | 0.8735   |
| 0.2258        | 3.0   | 225  | 0.2946          | 0.8802   |
| 0.1818        | 4.0   | 300  | 0.2874          | 0.8935   |
| 0.1414        | 5.0   | 375  | 0.3063          | 0.8869   |
| 0.1276        | 6.0   | 450  | 0.3088          | 0.8835   |
| 0.1278        | 7.0   | 525  | 0.3231          | 0.8885   |
| 0.0712        | 8.0   | 600  | 0.3560          | 0.8869   |
| 0.0461        | 9.0   | 675  | 0.3613          | 0.8918   |
| 0.0475        | 10.0  | 750  | 0.3784          | 0.8952   |
| 0.0242        | 11.0  | 825  | 0.4079          | 0.8885   |
| 0.0506        | 12.0  | 900  | 0.4429          | 0.8869   |
| 0.0272        | 13.0  | 975  | 0.4714          | 0.8869   |
| 0.0444        | 14.0  | 1050 | 0.5396          | 0.8802   |
| 0.0206        | 15.0  | 1125 | 0.5526          | 0.8735   |
| 0.0163        | 16.0  | 1200 | 0.5286          | 0.8852   |
| 0.0204        | 17.0  | 1275 | 0.5940          | 0.8819   |
| 0.0355        | 18.0  | 1350 | 0.5758          | 0.8769   |
| 0.0442        | 19.0  | 1425 | 0.5804          | 0.8785   |
| 0.0309        | 20.0  | 1500 | 0.5941          | 0.8819   |
| 0.0106        | 21.0  | 1575 | 0.6105          | 0.8802   |
| 0.0257        | 22.0  | 1650 | 0.6126          | 0.8835   |
| 0.0159        | 23.0  | 1725 | 0.6156          | 0.8852   |
| 0.0399        | 24.0  | 1800 | 0.6198          | 0.8785   |
| 0.0047        | 25.0  | 1875 | 0.6196          | 0.8819   |
| 0.0247        | 26.0  | 1950 | 0.6464          | 0.8835   |
| 0.024         | 27.0  | 2025 | 0.6527          | 0.8869   |
| 0.0438        | 28.0  | 2100 | 0.7050          | 0.8819   |
| 0.0088        | 29.0  | 2175 | 0.6605          | 0.8902   |
| 0.0182        | 30.0  | 2250 | 0.6570          | 0.8885   |
| 0.0251        | 31.0  | 2325 | 0.6796          | 0.8819   |
| 0.017         | 32.0  | 2400 | 0.6922          | 0.8852   |
| 0.033         | 33.0  | 2475 | 0.7245          | 0.8719   |
| 0.0216        | 34.0  | 2550 | 0.6972          | 0.8785   |
| 0.0144        | 35.0  | 2625 | 0.7562          | 0.8819   |
| 0.0161        | 36.0  | 2700 | 0.6986          | 0.8835   |
| 0.0005        | 37.0  | 2775 | 0.6981          | 0.8819   |
| 0.0053        | 38.0  | 2850 | 0.7088          | 0.8869   |
| 0.0506        | 39.0  | 2925 | 0.7290          | 0.8869   |
| 0.0237        | 40.0  | 3000 | 0.7146          | 0.8885   |
| 0.0005        | 41.0  | 3075 | 0.7241          | 0.8802   |
| 0.0171        | 42.0  | 3150 | 0.7294          | 0.8819   |
| 0.0152        | 43.0  | 3225 | 0.7178          | 0.8869   |
| 0.0007        | 44.0  | 3300 | 0.7168          | 0.8819   |
| 0.0066        | 45.0  | 3375 | 0.7243          | 0.8819   |
| 0.0023        | 46.0  | 3450 | 0.7324          | 0.8835   |
| 0.053         | 47.0  | 3525 | 0.7341          | 0.8852   |
| 0.0015        | 48.0  | 3600 | 0.7298          | 0.8852   |
| 0.0034        | 49.0  | 3675 | 0.7290          | 0.8852   |
| 0.0068        | 50.0  | 3750 | 0.7289          | 0.8852   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-base-patch16-224", "model-index": [{"name": "smids_1x_beit_base_adamax_00001_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 tensorboard safetensors beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us