End of training

README.md

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+---
+license: apache-2.0
+base_model: microsoft/beit-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: smids_3x_beit_base_sgd_001_fold5
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.38166666666666665
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# smids_3x_beit_base_sgd_001_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: 1.1851
+- Accuracy: 0.3817
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 1.2835        | 1.0   | 225   | 1.3130          | 0.3167   |
+| 1.3011        | 2.0   | 450   | 1.3069          | 0.3167   |
+| 1.243         | 3.0   | 675   | 1.3010          | 0.3217   |
+| 1.2411        | 4.0   | 900   | 1.2953          | 0.325    |
+| 1.2229        | 5.0   | 1125  | 1.2898          | 0.3233   |
+| 1.2191        | 6.0   | 1350  | 1.2846          | 0.3233   |
+| 1.2208        | 7.0   | 1575  | 1.2796          | 0.3233   |
+| 1.1965        | 8.0   | 1800  | 1.2748          | 0.3283   |
+| 1.2527        | 9.0   | 2025  | 1.2700          | 0.3333   |
+| 1.2362        | 10.0  | 2250  | 1.2655          | 0.335    |
+| 1.2197        | 11.0  | 2475  | 1.2613          | 0.335    |
+| 1.2149        | 12.0  | 2700  | 1.2570          | 0.34     |
+| 1.2002        | 13.0  | 2925  | 1.2530          | 0.3433   |
+| 1.1732        | 14.0  | 3150  | 1.2491          | 0.3483   |
+| 1.2252        | 15.0  | 3375  | 1.2454          | 0.35     |
+| 1.1628        | 16.0  | 3600  | 1.2417          | 0.3533   |
+| 1.1999        | 17.0  | 3825  | 1.2381          | 0.3583   |
+| 1.1844        | 18.0  | 4050  | 1.2348          | 0.3617   |
+| 1.1674        | 19.0  | 4275  | 1.2315          | 0.3617   |
+| 1.2258        | 20.0  | 4500  | 1.2284          | 0.36     |
+| 1.1214        | 21.0  | 4725  | 1.2254          | 0.3633   |
+| 1.151         | 22.0  | 4950  | 1.2225          | 0.365    |
+| 1.1693        | 23.0  | 5175  | 1.2197          | 0.3667   |
+| 1.1675        | 24.0  | 5400  | 1.2170          | 0.3667   |
+| 1.1534        | 25.0  | 5625  | 1.2144          | 0.3667   |
+| 1.1654        | 26.0  | 5850  | 1.2120          | 0.3667   |
+| 1.1707        | 27.0  | 6075  | 1.2097          | 0.3683   |
+| 1.1315        | 28.0  | 6300  | 1.2075          | 0.3683   |
+| 1.1501        | 29.0  | 6525  | 1.2054          | 0.37     |
+| 1.1251        | 30.0  | 6750  | 1.2034          | 0.37     |
+| 1.2017        | 31.0  | 6975  | 1.2016          | 0.3717   |
+| 1.0794        | 32.0  | 7200  | 1.1998          | 0.3717   |
+| 1.1172        | 33.0  | 7425  | 1.1981          | 0.3767   |
+| 1.1136        | 34.0  | 7650  | 1.1965          | 0.38     |
+| 1.1368        | 35.0  | 7875  | 1.1951          | 0.3817   |
+| 1.1416        | 36.0  | 8100  | 1.1937          | 0.38     |
+| 1.0723        | 37.0  | 8325  | 1.1925          | 0.3833   |
+| 1.0984        | 38.0  | 8550  | 1.1914          | 0.3833   |
+| 1.0812        | 39.0  | 8775  | 1.1903          | 0.3817   |
+| 1.1275        | 40.0  | 9000  | 1.1894          | 0.3817   |
+| 1.1166        | 41.0  | 9225  | 1.1885          | 0.3817   |
+| 1.1269        | 42.0  | 9450  | 1.1878          | 0.3817   |
+| 1.1329        | 43.0  | 9675  | 1.1871          | 0.3817   |
+| 1.1408        | 44.0  | 9900  | 1.1865          | 0.3817   |
+| 1.1416        | 45.0  | 10125 | 1.1861          | 0.3817   |
+| 1.1445        | 46.0  | 10350 | 1.1857          | 0.3817   |
+| 1.1225        | 47.0  | 10575 | 1.1854          | 0.3817   |
+| 1.1385        | 48.0  | 10800 | 1.1852          | 0.3817   |
+| 1.1537        | 49.0  | 11025 | 1.1851          | 0.3817   |
+| 1.1246        | 50.0  | 11250 | 1.1851          | 0.3817   |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.1.0+cu121
+- Datasets 2.12.0
+- Tokenizers 0.13.2

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