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_5x_deit_base_rms_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: 0.8066666666666666
+---
+
+<!-- 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_5x_deit_base_rms_001_fold5
+
+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: 0.5070
+- Accuracy: 0.8067
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 1.0986        | 1.0   | 375   | 1.0985          | 0.3517   |
+| 0.9277        | 2.0   | 750   | 0.8963          | 0.5367   |
+| 0.8868        | 3.0   | 1125  | 0.8248          | 0.5567   |
+| 0.8432        | 4.0   | 1500  | 0.8168          | 0.5367   |
+| 0.7756        | 5.0   | 1875  | 0.8234          | 0.555    |
+| 0.7692        | 6.0   | 2250  | 0.7291          | 0.6617   |
+| 0.7096        | 7.0   | 2625  | 0.7367          | 0.6533   |
+| 0.8553        | 8.0   | 3000  | 0.7548          | 0.63     |
+| 0.7339        | 9.0   | 3375  | 0.7547          | 0.6233   |
+| 0.6755        | 10.0  | 3750  | 0.7150          | 0.665    |
+| 0.7434        | 11.0  | 4125  | 0.7034          | 0.685    |
+| 0.6966        | 12.0  | 4500  | 0.6998          | 0.6833   |
+| 0.6638        | 13.0  | 4875  | 0.8188          | 0.615    |
+| 0.7005        | 14.0  | 5250  | 0.6380          | 0.7233   |
+| 0.7307        | 15.0  | 5625  | 0.6467          | 0.7017   |
+| 0.6252        | 16.0  | 6000  | 0.6189          | 0.7317   |
+| 0.6235        | 17.0  | 6375  | 0.5966          | 0.7267   |
+| 0.6067        | 18.0  | 6750  | 0.5889          | 0.7367   |
+| 0.6586        | 19.0  | 7125  | 0.5888          | 0.745    |
+| 0.553         | 20.0  | 7500  | 0.5461          | 0.7583   |
+| 0.5457        | 21.0  | 7875  | 0.5458          | 0.7717   |
+| 0.535         | 22.0  | 8250  | 0.5661          | 0.745    |
+| 0.5802        | 23.0  | 8625  | 0.5673          | 0.7633   |
+| 0.585         | 24.0  | 9000  | 0.5456          | 0.7767   |
+| 0.5034        | 25.0  | 9375  | 0.5600          | 0.7517   |
+| 0.519         | 26.0  | 9750  | 0.5101          | 0.7767   |
+| 0.578         | 27.0  | 10125 | 0.5562          | 0.7517   |
+| 0.5681        | 28.0  | 10500 | 0.5592          | 0.7633   |
+| 0.5613        | 29.0  | 10875 | 0.5207          | 0.7733   |
+| 0.4923        | 30.0  | 11250 | 0.5540          | 0.7683   |
+| 0.4514        | 31.0  | 11625 | 0.5170          | 0.795    |
+| 0.4948        | 32.0  | 12000 | 0.5569          | 0.775    |
+| 0.4729        | 33.0  | 12375 | 0.5006          | 0.7967   |
+| 0.4583        | 34.0  | 12750 | 0.5008          | 0.7917   |
+| 0.4376        | 35.0  | 13125 | 0.4986          | 0.815    |
+| 0.3894        | 36.0  | 13500 | 0.5048          | 0.8033   |
+| 0.4227        | 37.0  | 13875 | 0.5449          | 0.7883   |
+| 0.4237        | 38.0  | 14250 | 0.4850          | 0.81     |
+| 0.3609        | 39.0  | 14625 | 0.4881          | 0.8017   |
+| 0.4451        | 40.0  | 15000 | 0.5131          | 0.8067   |
+| 0.411         | 41.0  | 15375 | 0.5305          | 0.7983   |
+| 0.4629        | 42.0  | 15750 | 0.4959          | 0.8      |
+| 0.4034        | 43.0  | 16125 | 0.5125          | 0.8083   |
+| 0.3681        | 44.0  | 16500 | 0.5034          | 0.8033   |
+| 0.4332        | 45.0  | 16875 | 0.4946          | 0.8017   |
+| 0.3808        | 46.0  | 17250 | 0.4987          | 0.8067   |
+| 0.3828        | 47.0  | 17625 | 0.5113          | 0.8183   |
+| 0.2902        | 48.0  | 18000 | 0.5081          | 0.8      |
+| 0.3255        | 49.0  | 18375 | 0.5035          | 0.8083   |
+| 0.3922        | 50.0  | 18750 | 0.5070          | 0.8067   |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.1.0+cu121
+- Datasets 2.12.0
+- Tokenizers 0.13.2

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