End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: image_classification
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.5625
+---
+
+<!-- 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. -->
+
+# image_classification
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2386
+- Accuracy: 0.5625
+
+## 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: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 30
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.0874        | 1.0   | 10   | 2.0621          | 0.2313   |
+| 2.036         | 2.0   | 20   | 2.0392          | 0.2375   |
+| 1.9297        | 3.0   | 30   | 1.9592          | 0.3      |
+| 1.7723        | 4.0   | 40   | 1.7877          | 0.3937   |
+| 1.6184        | 5.0   | 50   | 1.6475          | 0.45     |
+| 1.5407        | 6.0   | 60   | 1.5514          | 0.4875   |
+| 1.4197        | 7.0   | 70   | 1.4967          | 0.4938   |
+| 1.3092        | 8.0   | 80   | 1.4332          | 0.4813   |
+| 1.1251        | 9.0   | 90   | 1.4457          | 0.4688   |
+| 1.2081        | 10.0  | 100  | 1.3603          | 0.4938   |
+| 0.9803        | 11.0  | 110  | 1.3501          | 0.5188   |
+| 1.0105        | 12.0  | 120  | 1.3212          | 0.55     |
+| 0.9264        | 13.0  | 130  | 1.2895          | 0.575    |
+| 0.9229        | 14.0  | 140  | 1.2882          | 0.5188   |
+| 0.9397        | 15.0  | 150  | 1.4027          | 0.475    |
+| 0.8322        | 16.0  | 160  | 1.2824          | 0.5312   |
+| 0.8185        | 17.0  | 170  | 1.3025          | 0.5      |
+| 0.7592        | 18.0  | 180  | 1.3629          | 0.475    |
+| 0.7416        | 19.0  | 190  | 1.3221          | 0.5437   |
+| 0.6323        | 20.0  | 200  | 1.2714          | 0.5563   |
+| 0.6453        | 21.0  | 210  | 1.3015          | 0.4938   |
+| 0.6049        | 22.0  | 220  | 1.3065          | 0.5375   |
+| 0.5919        | 23.0  | 230  | 1.2579          | 0.5375   |
+| 0.5354        | 24.0  | 240  | 1.2428          | 0.55     |
+| 0.6379        | 25.0  | 250  | 1.2884          | 0.5375   |
+| 0.5681        | 26.0  | 260  | 1.2201          | 0.5938   |
+| 0.4275        | 27.0  | 270  | 1.3199          | 0.4875   |
+| 0.4791        | 28.0  | 280  | 1.3027          | 0.5312   |
+| 0.4693        | 29.0  | 290  | 1.3737          | 0.4813   |
+| 0.5528        | 30.0  | 300  | 1.3342          | 0.4688   |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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