End of training

README.md

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+---
+license: apache-2.0
+base_model: facebook/deit-small-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: smids_10x_deit_small_rms_001_fold3
+  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: 0.7766666666666666
+---
+
+<!-- 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_small_rms_001_fold3
+
+This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5590
+- Accuracy: 0.7767
+
+## 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: 0.001
+- 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 0.8839        | 1.0   | 750   | 0.8956          | 0.4917   |
+| 0.8402        | 2.0   | 1500  | 0.8459          | 0.5383   |
+| 0.827         | 3.0   | 2250  | 0.8365          | 0.5417   |
+| 0.7595        | 4.0   | 3000  | 0.8404          | 0.5617   |
+| 0.8496        | 5.0   | 3750  | 0.9112          | 0.505    |
+| 0.7825        | 6.0   | 4500  | 0.8246          | 0.6233   |
+| 0.8185        | 7.0   | 5250  | 0.7843          | 0.6233   |
+| 0.7863        | 8.0   | 6000  | 0.7862          | 0.6183   |
+| 0.7304        | 9.0   | 6750  | 0.7478          | 0.6433   |
+| 0.7486        | 10.0  | 7500  | 0.7941          | 0.625    |
+| 0.7979        | 11.0  | 8250  | 0.7438          | 0.6817   |
+| 0.6928        | 12.0  | 9000  | 0.8898          | 0.58     |
+| 0.683         | 13.0  | 9750  | 0.7126          | 0.68     |
+| 0.7194        | 14.0  | 10500 | 0.7634          | 0.6367   |
+| 0.7001        | 15.0  | 11250 | 0.6906          | 0.68     |
+| 0.7209        | 16.0  | 12000 | 0.6988          | 0.675    |
+| 0.693         | 17.0  | 12750 | 0.7227          | 0.6733   |
+| 0.6594        | 18.0  | 13500 | 0.7119          | 0.675    |
+| 0.6733        | 19.0  | 14250 | 0.6769          | 0.695    |
+| 0.6368        | 20.0  | 15000 | 0.6310          | 0.7183   |
+| 0.5529        | 21.0  | 15750 | 0.6379          | 0.73     |
+| 0.674         | 22.0  | 16500 | 0.6200          | 0.7233   |
+| 0.6173        | 23.0  | 17250 | 0.6390          | 0.7117   |
+| 0.7017        | 24.0  | 18000 | 0.6234          | 0.7217   |
+| 0.6672        | 25.0  | 18750 | 0.6159          | 0.7117   |
+| 0.6143        | 26.0  | 19500 | 0.6119          | 0.7133   |
+| 0.5447        | 27.0  | 20250 | 0.6511          | 0.7      |
+| 0.616         | 28.0  | 21000 | 0.5943          | 0.7317   |
+| 0.6257        | 29.0  | 21750 | 0.6135          | 0.7417   |
+| 0.5784        | 30.0  | 22500 | 0.6236          | 0.7383   |
+| 0.5488        | 31.0  | 23250 | 0.5814          | 0.7483   |
+| 0.5683        | 32.0  | 24000 | 0.6409          | 0.725    |
+| 0.5657        | 33.0  | 24750 | 0.6193          | 0.7583   |
+| 0.7061        | 34.0  | 25500 | 0.7958          | 0.6533   |
+| 0.5815        | 35.0  | 26250 | 0.6092          | 0.7467   |
+| 0.545         | 36.0  | 27000 | 0.5902          | 0.7567   |
+| 0.574         | 37.0  | 27750 | 0.5865          | 0.7483   |
+| 0.5654        | 38.0  | 28500 | 0.6161          | 0.7467   |
+| 0.5393        | 39.0  | 29250 | 0.5677          | 0.7667   |
+| 0.6213        | 40.0  | 30000 | 0.5702          | 0.7633   |
+| 0.5565        | 41.0  | 30750 | 0.5675          | 0.75     |
+| 0.5323        | 42.0  | 31500 | 0.5645          | 0.7583   |
+| 0.5444        | 43.0  | 32250 | 0.5820          | 0.76     |
+| 0.4988        | 44.0  | 33000 | 0.5588          | 0.765    |
+| 0.5249        | 45.0  | 33750 | 0.5669          | 0.7583   |
+| 0.5246        | 46.0  | 34500 | 0.5504          | 0.7733   |
+| 0.4975        | 47.0  | 35250 | 0.5697          | 0.7717   |
+| 0.5083        | 48.0  | 36000 | 0.5554          | 0.7717   |
+| 0.4948        | 49.0  | 36750 | 0.5551          | 0.775    |
+| 0.4147        | 50.0  | 37500 | 0.5590          | 0.7767   |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.1.0+cu121
+- Datasets 2.12.0
+- Tokenizers 0.13.2