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# beit-base-patch16-224-65-fold5

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: 0.4937
- Accuracy: 0.9014

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9231  | 3    | 0.7742          | 0.4507   |
| No log        | 1.8462  | 6    | 0.7185          | 0.4930   |
| No log        | 2.7692  | 9    | 0.6625          | 0.5634   |
| 0.7338        | 4.0     | 13   | 0.6136          | 0.7183   |
| 0.7338        | 4.9231  | 16   | 0.5974          | 0.6479   |
| 0.7338        | 5.8462  | 19   | 0.5771          | 0.6338   |
| 0.6191        | 6.7692  | 22   | 0.5400          | 0.7042   |
| 0.6191        | 8.0     | 26   | 0.5127          | 0.7183   |
| 0.6191        | 8.9231  | 29   | 0.5341          | 0.7324   |
| 0.5723        | 9.8462  | 32   | 0.4877          | 0.7887   |
| 0.5723        | 10.7692 | 35   | 0.6659          | 0.6197   |
| 0.5723        | 12.0    | 39   | 0.5790          | 0.6761   |
| 0.5161        | 12.9231 | 42   | 0.5001          | 0.7606   |
| 0.5161        | 13.8462 | 45   | 0.4195          | 0.8310   |
| 0.5161        | 14.7692 | 48   | 0.4806          | 0.7746   |
| 0.4982        | 16.0    | 52   | 0.4013          | 0.8028   |
| 0.4982        | 16.9231 | 55   | 0.4189          | 0.8028   |
| 0.4982        | 17.8462 | 58   | 0.4018          | 0.8310   |
| 0.438         | 18.7692 | 61   | 0.5230          | 0.7183   |
| 0.438         | 20.0    | 65   | 0.4768          | 0.7465   |
| 0.438         | 20.9231 | 68   | 0.4428          | 0.7887   |
| 0.4641        | 21.8462 | 71   | 0.4122          | 0.8169   |
| 0.4641        | 22.7692 | 74   | 0.4537          | 0.7746   |
| 0.4641        | 24.0    | 78   | 0.3838          | 0.8310   |
| 0.308         | 24.9231 | 81   | 0.4586          | 0.8028   |
| 0.308         | 25.8462 | 84   | 0.5623          | 0.8028   |
| 0.308         | 26.7692 | 87   | 0.4050          | 0.8310   |
| 0.2766        | 28.0    | 91   | 0.3860          | 0.8169   |
| 0.2766        | 28.9231 | 94   | 0.4062          | 0.8169   |
| 0.2766        | 29.8462 | 97   | 0.6191          | 0.8169   |
| 0.288         | 30.7692 | 100  | 0.6076          | 0.7746   |
| 0.288         | 32.0    | 104  | 0.5300          | 0.8169   |
| 0.288         | 32.9231 | 107  | 0.6178          | 0.7606   |
| 0.2676        | 33.8462 | 110  | 0.4465          | 0.8451   |
| 0.2676        | 34.7692 | 113  | 0.5893          | 0.7606   |
| 0.2676        | 36.0    | 117  | 0.4782          | 0.8169   |
| 0.2306        | 36.9231 | 120  | 0.4946          | 0.8310   |
| 0.2306        | 37.8462 | 123  | 0.4534          | 0.8451   |
| 0.2306        | 38.7692 | 126  | 0.4603          | 0.8451   |
| 0.2095        | 40.0    | 130  | 0.5839          | 0.8028   |
| 0.2095        | 40.9231 | 133  | 0.4536          | 0.8310   |
| 0.2095        | 41.8462 | 136  | 0.4617          | 0.8592   |
| 0.2095        | 42.7692 | 139  | 0.4531          | 0.8592   |
| 0.2171        | 44.0    | 143  | 0.4325          | 0.8732   |
| 0.2171        | 44.9231 | 146  | 0.4732          | 0.8592   |
| 0.2171        | 45.8462 | 149  | 0.4779          | 0.8592   |
| 0.1686        | 46.7692 | 152  | 0.4841          | 0.8451   |
| 0.1686        | 48.0    | 156  | 0.5690          | 0.8310   |
| 0.1686        | 48.9231 | 159  | 0.5477          | 0.8451   |
| 0.1644        | 49.8462 | 162  | 0.5844          | 0.8310   |
| 0.1644        | 50.7692 | 165  | 0.5818          | 0.8310   |
| 0.1644        | 52.0    | 169  | 0.4674          | 0.8451   |
| 0.1915        | 52.9231 | 172  | 0.5320          | 0.8732   |
| 0.1915        | 53.8462 | 175  | 0.4933          | 0.8451   |
| 0.1915        | 54.7692 | 178  | 0.5090          | 0.8592   |
| 0.1561        | 56.0    | 182  | 0.4864          | 0.8451   |
| 0.1561        | 56.9231 | 185  | 0.4652          | 0.8732   |
| 0.1561        | 57.8462 | 188  | 0.5113          | 0.8592   |
| 0.1298        | 58.7692 | 191  | 0.4803          | 0.8732   |
| 0.1298        | 60.0    | 195  | 0.4794          | 0.8451   |
| 0.1298        | 60.9231 | 198  | 0.4743          | 0.8451   |
| 0.1467        | 61.8462 | 201  | 0.4739          | 0.8592   |
| 0.1467        | 62.7692 | 204  | 0.5211          | 0.8451   |
| 0.1467        | 64.0    | 208  | 0.5315          | 0.8592   |
| 0.1363        | 64.9231 | 211  | 0.5182          | 0.8592   |
| 0.1363        | 65.8462 | 214  | 0.5160          | 0.8451   |
| 0.1363        | 66.7692 | 217  | 0.6170          | 0.8169   |
| 0.154         | 68.0    | 221  | 0.4857          | 0.8592   |
| 0.154         | 68.9231 | 224  | 0.4763          | 0.8592   |
| 0.154         | 69.8462 | 227  | 0.4937          | 0.9014   |
| 0.141         | 70.7692 | 230  | 0.5038          | 0.8873   |
| 0.141         | 72.0    | 234  | 0.5026          | 0.8592   |
| 0.141         | 72.9231 | 237  | 0.5019          | 0.8592   |
| 0.1166        | 73.8462 | 240  | 0.5028          | 0.8592   |
| 0.1166        | 74.7692 | 243  | 0.5226          | 0.8592   |
| 0.1166        | 76.0    | 247  | 0.5295          | 0.8732   |
| 0.117         | 76.9231 | 250  | 0.5073          | 0.8732   |
| 0.117         | 77.8462 | 253  | 0.5081          | 0.8732   |
| 0.117         | 78.7692 | 256  | 0.5036          | 0.8592   |
| 0.1037        | 80.0    | 260  | 0.5038          | 0.8451   |
| 0.1037        | 80.9231 | 263  | 0.5072          | 0.8451   |
| 0.1037        | 81.8462 | 266  | 0.5081          | 0.8451   |
| 0.1037        | 82.7692 | 269  | 0.5062          | 0.8310   |
| 0.1085        | 84.0    | 273  | 0.5144          | 0.8451   |
| 0.1085        | 84.9231 | 276  | 0.5208          | 0.8592   |
| 0.1085        | 85.8462 | 279  | 0.5248          | 0.8592   |
| 0.0939        | 86.7692 | 282  | 0.5301          | 0.8592   |
| 0.0939        | 88.0    | 286  | 0.5357          | 0.8451   |
| 0.0939        | 88.9231 | 289  | 0.5398          | 0.8451   |
| 0.0962        | 89.8462 | 292  | 0.5434          | 0.8451   |
| 0.0962        | 90.7692 | 295  | 0.5455          | 0.8451   |
| 0.0962        | 92.0    | 299  | 0.5448          | 0.8451   |
| 0.1131        | 92.3077 | 300  | 0.5446          | 0.8451   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

hasta normal