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: emotion_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: 0.55625
+---
+
+<!-- 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. -->
+
+# emotion_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: 1.2963
+- Accuracy: 0.5563
+
+## 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: cosine_with_restarts
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 40
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.0771        | 1.0   | 10   | 2.0698          | 0.1375   |
+| 2.0613        | 2.0   | 20   | 2.0368          | 0.2875   |
+| 2.0214        | 3.0   | 30   | 2.0010          | 0.2625   |
+| 1.9314        | 4.0   | 40   | 1.8913          | 0.3      |
+| 1.785         | 5.0   | 50   | 1.7270          | 0.375    |
+| 1.6343        | 6.0   | 60   | 1.6009          | 0.4313   |
+| 1.5327        | 7.0   | 70   | 1.5766          | 0.3937   |
+| 1.452         | 8.0   | 80   | 1.4714          | 0.475    |
+| 1.38          | 9.0   | 90   | 1.4570          | 0.4688   |
+| 1.3061        | 10.0  | 100  | 1.4357          | 0.4688   |
+| 1.2331        | 11.0  | 110  | 1.3691          | 0.4938   |
+| 1.1784        | 12.0  | 120  | 1.3377          | 0.4813   |
+| 1.1049        | 13.0  | 130  | 1.2982          | 0.5625   |
+| 1.0938        | 14.0  | 140  | 1.2847          | 0.5188   |
+| 1.0191        | 15.0  | 150  | 1.2630          | 0.575    |
+| 0.9665        | 16.0  | 160  | 1.3427          | 0.4938   |
+| 0.9028        | 17.0  | 170  | 1.3189          | 0.525    |
+| 0.886         | 18.0  | 180  | 1.2599          | 0.5312   |
+| 0.8272        | 19.0  | 190  | 1.3148          | 0.525    |
+| 0.7923        | 20.0  | 200  | 1.2634          | 0.55     |
+| 0.8033        | 21.0  | 210  | 1.2664          | 0.5625   |
+| 0.724         | 22.0  | 220  | 1.2286          | 0.525    |
+| 0.6966        | 23.0  | 230  | 1.3408          | 0.5375   |
+| 0.6722        | 24.0  | 240  | 1.3032          | 0.5062   |
+| 0.6816        | 25.0  | 250  | 1.3318          | 0.5062   |
+| 0.6162        | 26.0  | 260  | 1.3775          | 0.4938   |
+| 0.6099        | 27.0  | 270  | 1.2903          | 0.5437   |
+| 0.5786        | 28.0  | 280  | 1.2361          | 0.6      |
+| 0.5931        | 29.0  | 290  | 1.2998          | 0.5312   |
+| 0.5849        | 30.0  | 300  | 1.3221          | 0.5062   |
+| 0.5606        | 31.0  | 310  | 1.2756          | 0.5125   |
+| 0.5561        | 32.0  | 320  | 1.3732          | 0.4813   |
+| 0.547         | 33.0  | 330  | 1.3308          | 0.5375   |
+| 0.5405        | 34.0  | 340  | 1.3506          | 0.5062   |
+| 0.5419        | 35.0  | 350  | 1.2487          | 0.5625   |
+| 0.5168        | 36.0  | 360  | 1.2269          | 0.525    |
+| 0.5361        | 37.0  | 370  | 1.2993          | 0.55     |
+| 0.5375        | 38.0  | 380  | 1.2806          | 0.575    |
+| 0.5235        | 39.0  | 390  | 1.3404          | 0.5188   |
+| 0.5318        | 40.0  | 400  | 1.3315          | 0.4938   |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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