End of training

README.md

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+---
+license: apache-2.0
+base_model: facebook/deit-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: smids_10x_deit_base_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: 0.5540765391014975
+---
+
+<!-- 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_deit_base_sgd_00001_fold2
+
+This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0091
+- Accuracy: 0.5541
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 1.1301        | 1.0   | 750   | 1.1044          | 0.3361   |
+| 1.0956        | 2.0   | 1500  | 1.1008          | 0.3394   |
+| 1.0885        | 3.0   | 2250  | 1.0973          | 0.3527   |
+| 1.0813        | 4.0   | 3000  | 1.0939          | 0.3677   |
+| 1.0897        | 5.0   | 3750  | 1.0907          | 0.3794   |
+| 1.0999        | 6.0   | 4500  | 1.0875          | 0.3794   |
+| 1.0837        | 7.0   | 5250  | 1.0844          | 0.3810   |
+| 1.0633        | 8.0   | 6000  | 1.0813          | 0.3860   |
+| 1.0777        | 9.0   | 6750  | 1.0784          | 0.3977   |
+| 1.0672        | 10.0  | 7500  | 1.0755          | 0.4027   |
+| 1.0688        | 11.0  | 8250  | 1.0726          | 0.4143   |
+| 1.0524        | 12.0  | 9000  | 1.0698          | 0.4193   |
+| 1.0588        | 13.0  | 9750  | 1.0670          | 0.4276   |
+| 1.0355        | 14.0  | 10500 | 1.0642          | 0.4443   |
+| 1.0421        | 15.0  | 11250 | 1.0615          | 0.4526   |
+| 1.0517        | 16.0  | 12000 | 1.0588          | 0.4526   |
+| 1.0228        | 17.0  | 12750 | 1.0561          | 0.4609   |
+| 1.052         | 18.0  | 13500 | 1.0535          | 0.4626   |
+| 1.0453        | 19.0  | 14250 | 1.0509          | 0.4742   |
+| 1.0307        | 20.0  | 15000 | 1.0483          | 0.4842   |
+| 1.0308        | 21.0  | 15750 | 1.0459          | 0.4892   |
+| 1.0369        | 22.0  | 16500 | 1.0434          | 0.5008   |
+| 1.0173        | 23.0  | 17250 | 1.0411          | 0.5008   |
+| 1.0178        | 24.0  | 18000 | 1.0388          | 0.5058   |
+| 1.021         | 25.0  | 18750 | 1.0366          | 0.5075   |
+| 1.0167        | 26.0  | 19500 | 1.0344          | 0.5075   |
+| 1.0247        | 27.0  | 20250 | 1.0323          | 0.5125   |
+| 1.0234        | 28.0  | 21000 | 1.0303          | 0.5208   |
+| 1.003         | 29.0  | 21750 | 1.0283          | 0.5258   |
+| 1.008         | 30.0  | 22500 | 1.0265          | 0.5308   |
+| 1.0192        | 31.0  | 23250 | 1.0247          | 0.5341   |
+| 1.0098        | 32.0  | 24000 | 1.0231          | 0.5374   |
+| 0.9987        | 33.0  | 24750 | 1.0215          | 0.5408   |
+| 0.9994        | 34.0  | 25500 | 1.0200          | 0.5408   |
+| 1.0171        | 35.0  | 26250 | 1.0186          | 0.5408   |
+| 1.013         | 36.0  | 27000 | 1.0173          | 0.5441   |
+| 0.988         | 37.0  | 27750 | 1.0161          | 0.5441   |
+| 0.9905        | 38.0  | 28500 | 1.0150          | 0.5491   |
+| 0.9967        | 39.0  | 29250 | 1.0140          | 0.5491   |
+| 0.9901        | 40.0  | 30000 | 1.0131          | 0.5557   |
+| 0.9977        | 41.0  | 30750 | 1.0123          | 0.5557   |
+| 1.0045        | 42.0  | 31500 | 1.0116          | 0.5557   |
+| 0.9983        | 43.0  | 32250 | 1.0109          | 0.5541   |
+| 0.9818        | 44.0  | 33000 | 1.0104          | 0.5541   |
+| 0.9768        | 45.0  | 33750 | 1.0100          | 0.5541   |
+| 0.9827        | 46.0  | 34500 | 1.0096          | 0.5541   |
+| 0.9904        | 47.0  | 35250 | 1.0094          | 0.5541   |
+| 0.9884        | 48.0  | 36000 | 1.0092          | 0.5541   |
+| 0.9851        | 49.0  | 36750 | 1.0091          | 0.5541   |
+| 0.9904        | 50.0  | 37500 | 1.0091          | 0.5541   |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.1.0+cu121
+- Datasets 2.12.0
+- Tokenizers 0.13.2

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