End of training

README.md

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+---
+license: apache-2.0
+base_model: microsoft/beit-large-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: smids_10x_beit_large_sgd_00001_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: 0.6316666666666667
+---
+
+<!-- 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_10x_beit_large_sgd_00001_fold5
+
+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: 0.8273
+- Accuracy: 0.6317
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 1.2351        | 1.0   | 750   | 1.2384          | 0.335    |
+| 1.188         | 2.0   | 1500  | 1.2067          | 0.3417   |
+| 1.1425        | 3.0   | 2250  | 1.1794          | 0.35     |
+| 1.0663        | 4.0   | 3000  | 1.1549          | 0.36     |
+| 1.0302        | 5.0   | 3750  | 1.1332          | 0.3733   |
+| 1.0803        | 6.0   | 4500  | 1.1133          | 0.3783   |
+| 1.0194        | 7.0   | 5250  | 1.0948          | 0.395    |
+| 1.041         | 8.0   | 6000  | 1.0776          | 0.4133   |
+| 0.958         | 9.0   | 6750  | 1.0617          | 0.4267   |
+| 0.9328        | 10.0  | 7500  | 1.0465          | 0.44     |
+| 0.9293        | 11.0  | 8250  | 1.0324          | 0.4533   |
+| 0.9087        | 12.0  | 9000  | 1.0189          | 0.465    |
+| 0.9387        | 13.0  | 9750  | 1.0063          | 0.4783   |
+| 0.8996        | 14.0  | 10500 | 0.9944          | 0.4933   |
+| 0.8606        | 15.0  | 11250 | 0.9830          | 0.5083   |
+| 0.8536        | 16.0  | 12000 | 0.9723          | 0.5117   |
+| 0.8222        | 17.0  | 12750 | 0.9621          | 0.5217   |
+| 0.8298        | 18.0  | 13500 | 0.9525          | 0.53     |
+| 0.9106        | 19.0  | 14250 | 0.9434          | 0.54     |
+| 0.8462        | 20.0  | 15000 | 0.9347          | 0.5483   |
+| 0.8209        | 21.0  | 15750 | 0.9265          | 0.5533   |
+| 0.8393        | 22.0  | 16500 | 0.9186          | 0.5583   |
+| 0.8236        | 23.0  | 17250 | 0.9111          | 0.565    |
+| 0.8476        | 24.0  | 18000 | 0.9042          | 0.5717   |
+| 0.7925        | 25.0  | 18750 | 0.8975          | 0.5733   |
+| 0.8294        | 26.0  | 19500 | 0.8913          | 0.5817   |
+| 0.7415        | 27.0  | 20250 | 0.8856          | 0.585    |
+| 0.8113        | 28.0  | 21000 | 0.8800          | 0.585    |
+| 0.8087        | 29.0  | 21750 | 0.8747          | 0.5833   |
+| 0.8087        | 30.0  | 22500 | 0.8698          | 0.59     |
+| 0.7723        | 31.0  | 23250 | 0.8652          | 0.595    |
+| 0.7864        | 32.0  | 24000 | 0.8609          | 0.6033   |
+| 0.7882        | 33.0  | 24750 | 0.8569          | 0.6083   |
+| 0.7814        | 34.0  | 25500 | 0.8532          | 0.61     |
+| 0.8053        | 35.0  | 26250 | 0.8498          | 0.6117   |
+| 0.7759        | 36.0  | 27000 | 0.8467          | 0.6167   |
+| 0.73          | 37.0  | 27750 | 0.8438          | 0.6167   |
+| 0.8437        | 38.0  | 28500 | 0.8412          | 0.6183   |
+| 0.7621        | 39.0  | 29250 | 0.8389          | 0.6183   |
+| 0.719         | 40.0  | 30000 | 0.8367          | 0.6217   |
+| 0.7491        | 41.0  | 30750 | 0.8348          | 0.625    |
+| 0.7887        | 42.0  | 31500 | 0.8332          | 0.625    |
+| 0.8254        | 43.0  | 32250 | 0.8317          | 0.625    |
+| 0.7337        | 44.0  | 33000 | 0.8305          | 0.6267   |
+| 0.7762        | 45.0  | 33750 | 0.8295          | 0.6283   |
+| 0.7277        | 46.0  | 34500 | 0.8286          | 0.6317   |
+| 0.7733        | 47.0  | 35250 | 0.8280          | 0.6317   |
+| 0.7249        | 48.0  | 36000 | 0.8276          | 0.6317   |
+| 0.7591        | 49.0  | 36750 | 0.8274          | 0.6317   |
+| 0.7103        | 50.0  | 37500 | 0.8273          | 0.6317   |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.1.0+cu121
+- Datasets 2.12.0
+- Tokenizers 0.13.2

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