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# smids_1x_beit_base_adamax_001_fold4

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: 1.7646
- Accuracy: 0.775

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9222        | 1.0   | 75   | 0.8216          | 0.5567   |
| 0.8457        | 2.0   | 150  | 0.8398          | 0.57     |
| 0.8147        | 3.0   | 225  | 0.7493          | 0.6333   |
| 0.7701        | 4.0   | 300  | 0.7606          | 0.6117   |
| 0.8026        | 5.0   | 375  | 0.8189          | 0.565    |
| 0.6963        | 6.0   | 450  | 0.6808          | 0.665    |
| 0.7638        | 7.0   | 525  | 0.6641          | 0.7017   |
| 0.6601        | 8.0   | 600  | 0.6495          | 0.6833   |
| 0.6719        | 9.0   | 675  | 0.7134          | 0.66     |
| 0.5461        | 10.0  | 750  | 0.5791          | 0.7483   |
| 0.547         | 11.0  | 825  | 0.5859          | 0.7633   |
| 0.4912        | 12.0  | 900  | 0.5937          | 0.735    |
| 0.5352        | 13.0  | 975  | 0.5233          | 0.7667   |
| 0.4434        | 14.0  | 1050 | 0.5543          | 0.7617   |
| 0.4927        | 15.0  | 1125 | 0.7581          | 0.6767   |
| 0.4312        | 16.0  | 1200 | 0.5587          | 0.7667   |
| 0.3899        | 17.0  | 1275 | 0.6422          | 0.7633   |
| 0.3786        | 18.0  | 1350 | 0.6068          | 0.7783   |
| 0.4006        | 19.0  | 1425 | 0.6778          | 0.7617   |
| 0.3094        | 20.0  | 1500 | 0.6494          | 0.775    |
| 0.3319        | 21.0  | 1575 | 0.6363          | 0.765    |
| 0.2928        | 22.0  | 1650 | 0.7276          | 0.7817   |
| 0.2846        | 23.0  | 1725 | 0.8156          | 0.7733   |
| 0.1736        | 24.0  | 1800 | 0.7838          | 0.785    |
| 0.2416        | 25.0  | 1875 | 0.8283          | 0.775    |
| 0.1805        | 26.0  | 1950 | 0.8042          | 0.7867   |
| 0.1895        | 27.0  | 2025 | 1.0411          | 0.7933   |
| 0.0832        | 28.0  | 2100 | 1.0766          | 0.7983   |
| 0.099         | 29.0  | 2175 | 1.1178          | 0.7683   |
| 0.0916        | 30.0  | 2250 | 1.3040          | 0.775    |
| 0.128         | 31.0  | 2325 | 1.2237          | 0.7983   |
| 0.0775        | 32.0  | 2400 | 1.1999          | 0.79     |
| 0.0706        | 33.0  | 2475 | 1.4034          | 0.78     |
| 0.0546        | 34.0  | 2550 | 1.4009          | 0.785    |
| 0.0453        | 35.0  | 2625 | 1.2357          | 0.7917   |
| 0.0136        | 36.0  | 2700 | 1.4685          | 0.79     |
| 0.0534        | 37.0  | 2775 | 1.8215          | 0.7717   |
| 0.0751        | 38.0  | 2850 | 1.6150          | 0.7833   |
| 0.0013        | 39.0  | 2925 | 1.7207          | 0.7917   |
| 0.0466        | 40.0  | 3000 | 1.4737          | 0.785    |
| 0.0122        | 41.0  | 3075 | 1.5635          | 0.7783   |
| 0.0071        | 42.0  | 3150 | 1.6935          | 0.7783   |
| 0.0119        | 43.0  | 3225 | 1.6935          | 0.7833   |
| 0.0065        | 44.0  | 3300 | 1.7015          | 0.7883   |
| 0.0254        | 45.0  | 3375 | 1.7329          | 0.7867   |
| 0.0205        | 46.0  | 3450 | 1.6886          | 0.785    |
| 0.0082        | 47.0  | 3525 | 1.7094          | 0.7833   |
| 0.0134        | 48.0  | 3600 | 1.7793          | 0.78     |
| 0.005         | 49.0  | 3675 | 1.7866          | 0.7767   |
| 0.0132        | 50.0  | 3750 | 1.7646          | 0.775    |


### 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_001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.775, "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