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_fold2
+  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: 0.6472545757071547
+---
+
+<!-- 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_fold2
+
+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: 0.7989
+- Accuracy: 0.6473
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 1.1712        | 1.0   | 750   | 1.2053          | 0.3594   |
+| 1.1095        | 2.0   | 1500  | 1.1744          | 0.3677   |
+| 1.079         | 3.0   | 2250  | 1.1476          | 0.3677   |
+| 1.0868        | 4.0   | 3000  | 1.1238          | 0.3760   |
+| 1.0188        | 5.0   | 3750  | 1.1026          | 0.4043   |
+| 1.0313        | 6.0   | 4500  | 1.0830          | 0.4176   |
+| 0.9867        | 7.0   | 5250  | 1.0650          | 0.4343   |
+| 0.9922        | 8.0   | 6000  | 1.0482          | 0.4509   |
+| 1.0089        | 9.0   | 6750  | 1.0324          | 0.4626   |
+| 0.9248        | 10.0  | 7500  | 1.0176          | 0.4809   |
+| 0.9924        | 11.0  | 8250  | 1.0037          | 0.4942   |
+| 0.9341        | 12.0  | 9000  | 0.9905          | 0.5042   |
+| 0.9032        | 13.0  | 9750  | 0.9777          | 0.5158   |
+| 0.9223        | 14.0  | 10500 | 0.9658          | 0.5241   |
+| 0.8875        | 15.0  | 11250 | 0.9546          | 0.5275   |
+| 0.8812        | 16.0  | 12000 | 0.9440          | 0.5408   |
+| 0.8383        | 17.0  | 12750 | 0.9339          | 0.5524   |
+| 0.8368        | 18.0  | 13500 | 0.9242          | 0.5557   |
+| 0.8681        | 19.0  | 14250 | 0.9150          | 0.5657   |
+| 0.8552        | 20.0  | 15000 | 0.9065          | 0.5674   |
+| 0.8564        | 21.0  | 15750 | 0.8983          | 0.5691   |
+| 0.8254        | 22.0  | 16500 | 0.8905          | 0.5740   |
+| 0.842         | 23.0  | 17250 | 0.8831          | 0.5807   |
+| 0.802         | 24.0  | 18000 | 0.8761          | 0.5857   |
+| 0.8617        | 25.0  | 18750 | 0.8694          | 0.5973   |
+| 0.8384        | 26.0  | 19500 | 0.8631          | 0.6057   |
+| 0.8257        | 27.0  | 20250 | 0.8572          | 0.6106   |
+| 0.8327        | 28.0  | 21000 | 0.8516          | 0.6156   |
+| 0.8111        | 29.0  | 21750 | 0.8464          | 0.6173   |
+| 0.7892        | 30.0  | 22500 | 0.8414          | 0.6206   |
+| 0.7974        | 31.0  | 23250 | 0.8368          | 0.6256   |
+| 0.8791        | 32.0  | 24000 | 0.8325          | 0.6256   |
+| 0.7583        | 33.0  | 24750 | 0.8285          | 0.6306   |
+| 0.7714        | 34.0  | 25500 | 0.8248          | 0.6323   |
+| 0.7891        | 35.0  | 26250 | 0.8214          | 0.6356   |
+| 0.7659        | 36.0  | 27000 | 0.8182          | 0.6389   |
+| 0.8096        | 37.0  | 27750 | 0.8154          | 0.6356   |
+| 0.7644        | 38.0  | 28500 | 0.8128          | 0.6373   |
+| 0.8029        | 39.0  | 29250 | 0.8104          | 0.6406   |
+| 0.7912        | 40.0  | 30000 | 0.8082          | 0.6406   |
+| 0.7766        | 41.0  | 30750 | 0.8063          | 0.6423   |
+| 0.7693        | 42.0  | 31500 | 0.8047          | 0.6439   |
+| 0.735         | 43.0  | 32250 | 0.8032          | 0.6456   |
+| 0.7637        | 44.0  | 33000 | 0.8020          | 0.6456   |
+| 0.7733        | 45.0  | 33750 | 0.8010          | 0.6473   |
+| 0.7268        | 46.0  | 34500 | 0.8002          | 0.6473   |
+| 0.8097        | 47.0  | 35250 | 0.7996          | 0.6473   |
+| 0.7648        | 48.0  | 36000 | 0.7991          | 0.6473   |
+| 0.7593        | 49.0  | 36750 | 0.7989          | 0.6473   |
+| 0.7579        | 50.0  | 37500 | 0.7989          | 0.6473   |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.1.0+cu121
+- Datasets 2.12.0
+- Tokenizers 0.13.2

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