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_model
+  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: 0.6
+---
+
+<!-- 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_model
+
+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: 1.3497
+- Accuracy: 0.6
+
+## 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: 0.0001
+- 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: 50
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.0823        | 1.0   | 10   | 2.0560          | 0.1625   |
+| 2.0479        | 2.0   | 20   | 2.0218          | 0.2812   |
+| 1.9636        | 3.0   | 30   | 1.8882          | 0.4062   |
+| 1.7902        | 4.0   | 40   | 1.6881          | 0.4313   |
+| 1.5792        | 5.0   | 50   | 1.6159          | 0.3688   |
+| 1.4429        | 6.0   | 60   | 1.3871          | 0.5687   |
+| 1.2854        | 7.0   | 70   | 1.2973          | 0.5437   |
+| 1.1487        | 8.0   | 80   | 1.2303          | 0.6      |
+| 1.0374        | 9.0   | 90   | 1.2661          | 0.5375   |
+| 0.9584        | 10.0  | 100  | 1.1662          | 0.5563   |
+| 0.8108        | 11.0  | 110  | 1.2135          | 0.5312   |
+| 0.7402        | 12.0  | 120  | 1.2117          | 0.5813   |
+| 0.6349        | 13.0  | 130  | 1.1176          | 0.6062   |
+| 0.5674        | 14.0  | 140  | 1.1794          | 0.575    |
+| 0.5103        | 15.0  | 150  | 1.0948          | 0.6375   |
+| 0.4826        | 16.0  | 160  | 1.1833          | 0.5875   |
+| 0.4128        | 17.0  | 170  | 1.2601          | 0.5375   |
+| 0.3664        | 18.0  | 180  | 1.3378          | 0.55     |
+| 0.3112        | 19.0  | 190  | 1.2789          | 0.5437   |
+| 0.335         | 20.0  | 200  | 1.2913          | 0.5625   |
+| 0.3261        | 21.0  | 210  | 1.1114          | 0.6      |
+| 0.3443        | 22.0  | 220  | 1.2177          | 0.5938   |
+| 0.2642        | 23.0  | 230  | 1.2299          | 0.5938   |
+| 0.2895        | 24.0  | 240  | 1.2339          | 0.5813   |
+| 0.266         | 25.0  | 250  | 1.2384          | 0.5875   |
+| 0.2725        | 26.0  | 260  | 1.2100          | 0.6062   |
+| 0.2725        | 27.0  | 270  | 1.3073          | 0.575    |
+| 0.2637        | 28.0  | 280  | 1.3019          | 0.5875   |
+| 0.2561        | 29.0  | 290  | 1.3597          | 0.5437   |
+| 0.2375        | 30.0  | 300  | 1.3404          | 0.5563   |
+| 0.2188        | 31.0  | 310  | 1.2922          | 0.5813   |
+| 0.2141        | 32.0  | 320  | 1.3778          | 0.5312   |
+| 0.198         | 33.0  | 330  | 1.3473          | 0.5875   |
+| 0.1805        | 34.0  | 340  | 1.3984          | 0.5437   |
+| 0.1888        | 35.0  | 350  | 1.3508          | 0.5813   |
+| 0.1867        | 36.0  | 360  | 1.3531          | 0.575    |
+| 0.1596        | 37.0  | 370  | 1.5846          | 0.4875   |
+| 0.1564        | 38.0  | 380  | 1.3380          | 0.5687   |
+| 0.1719        | 39.0  | 390  | 1.5206          | 0.5312   |
+| 0.1678        | 40.0  | 400  | 1.2929          | 0.5875   |
+| 0.136         | 41.0  | 410  | 1.5031          | 0.55     |
+| 0.1602        | 42.0  | 420  | 1.3855          | 0.5625   |
+| 0.174         | 43.0  | 430  | 1.4385          | 0.5875   |
+| 0.179         | 44.0  | 440  | 1.3153          | 0.575    |
+| 0.1284        | 45.0  | 450  | 1.4295          | 0.5875   |
+| 0.1419        | 46.0  | 460  | 1.4126          | 0.575    |
+| 0.1425        | 47.0  | 470  | 1.3760          | 0.5687   |
+| 0.1602        | 48.0  | 480  | 1.4374          | 0.5875   |
+| 0.1473        | 49.0  | 490  | 1.3126          | 0.5813   |
+| 0.153         | 50.0  | 500  | 1.3497          | 0.6      |
+
+
+### Framework versions
+
+- Transformers 4.33.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3

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