End of training

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: emotion_recognition
+  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: 0.6125
+---
+
+<!-- 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_recognition
+
+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: 1.2014
+- Accuracy: 0.6125
+
+## 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: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.0842        | 1.0   | 10   | 2.0668          | 0.175    |
+| 2.039         | 2.0   | 20   | 2.0070          | 0.2875   |
+| 1.9285        | 3.0   | 30   | 1.8789          | 0.4062   |
+| 1.7699        | 4.0   | 40   | 1.6942          | 0.425    |
+| 1.6135        | 5.0   | 50   | 1.5758          | 0.4313   |
+| 1.5056        | 6.0   | 60   | 1.4884          | 0.55     |
+| 1.3896        | 7.0   | 70   | 1.3999          | 0.5437   |
+| 1.2804        | 8.0   | 80   | 1.3563          | 0.5437   |
+| 1.2043        | 9.0   | 90   | 1.3244          | 0.55     |
+| 1.1231        | 10.0  | 100  | 1.2775          | 0.6062   |
+| 1.0652        | 11.0  | 110  | 1.2567          | 0.575    |
+| 1.0005        | 12.0  | 120  | 1.2833          | 0.5563   |
+| 0.9878        | 13.0  | 130  | 1.2277          | 0.5687   |
+| 0.9714        | 14.0  | 140  | 1.2557          | 0.5563   |
+| 0.9057        | 15.0  | 150  | 1.2187          | 0.6125   |
+| 0.8854        | 16.0  | 160  | 1.2612          | 0.5437   |
+| 0.8478        | 17.0  | 170  | 1.2450          | 0.5437   |
+| 0.8601        | 18.0  | 180  | 1.2456          | 0.5375   |
+| 0.8498        | 19.0  | 190  | 1.2413          | 0.5875   |
+| 0.8775        | 20.0  | 200  | 1.1928          | 0.6      |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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