End of training

README.md

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+---
+library_name: transformers
+base_model: motheecreator/vit-Facial-Expression-Recognition
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: vit-Facial-Expression-Recognition
+  results: []
+---
+
+<!-- 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. -->
+
+# vit-Facial-Expression-Recognition
+
+This model is a fine-tuned version of [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/vit-Facial-Expression-Recognition) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3705
+- Accuracy: 0.8735
+
+## 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: 3e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 256
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 1000
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 4.5195        | 0.2164 | 100  | 0.3776          | 0.8729   |
+| 4.5328        | 0.4328 | 200  | 0.3786          | 0.8718   |
+| 4.554         | 0.6492 | 300  | 0.3800          | 0.8717   |
+| 4.5812        | 0.8656 | 400  | 0.3764          | 0.8739   |
+| 4.2724        | 1.0801 | 500  | 0.3793          | 0.8722   |
+| 4.5232        | 1.2965 | 600  | 0.3833          | 0.8693   |
+| 4.4717        | 1.5128 | 700  | 0.3864          | 0.8684   |
+| 4.4636        | 1.7292 | 800  | 0.3875          | 0.8676   |
+| 4.5234        | 1.9456 | 900  | 0.3897          | 0.8667   |
+| 4.2156        | 2.1601 | 1000 | 0.3993          | 0.8632   |
+| 4.063         | 2.3765 | 1100 | 0.3934          | 0.8651   |
+| 4.1068        | 2.5929 | 1200 | 0.3823          | 0.8702   |
+| 3.9902        | 2.8093 | 1300 | 0.3724          | 0.8734   |
+
+
+### Framework versions
+
+- Transformers 4.47.1
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.21.0

all_results.json

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+{
+    "epoch": 2.995401677035434,
+    "eval_accuracy": 0.8734528238079134,
+    "eval_loss": 0.3704567551612854,
+    "eval_runtime": 362.8359,
+    "eval_samples_per_second": 81.497,
+    "eval_steps_per_second": 2.549
+}

eval_results.json

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+{
+    "epoch": 2.995401677035434,
+    "eval_accuracy": 0.8734528238079134,
+    "eval_loss": 0.3704567551612854,
+    "eval_runtime": 362.8359,
+    "eval_samples_per_second": 81.497,
+    "eval_steps_per_second": 2.549
+}