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emotion_classification
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.2383
- 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: 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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.0769 | 1.0 | 10 | 2.0617 | 0.1812 |
| 2.0383 | 2.0 | 20 | 2.0104 | 0.3 |
| 1.9423 | 3.0 | 30 | 1.8932 | 0.425 |
| 1.7923 | 4.0 | 40 | 1.7442 | 0.475 |
| 1.6547 | 5.0 | 50 | 1.6047 | 0.4875 |
| 1.5297 | 6.0 | 60 | 1.5184 | 0.5437 |
| 1.4345 | 7.0 | 70 | 1.4392 | 0.5625 |
| 1.337 | 8.0 | 80 | 1.3847 | 0.5875 |
| 1.2722 | 9.0 | 90 | 1.3442 | 0.55 |
| 1.217 | 10.0 | 100 | 1.3058 | 0.5625 |
| 1.1497 | 11.0 | 110 | 1.2914 | 0.55 |
| 1.0977 | 12.0 | 120 | 1.2377 | 0.6125 |
| 1.0507 | 13.0 | 130 | 1.2253 | 0.5687 |
| 1.0268 | 14.0 | 140 | 1.2269 | 0.5938 |
| 0.967 | 15.0 | 150 | 1.2260 | 0.5938 |
| 0.9269 | 16.0 | 160 | 1.2421 | 0.5687 |
| 0.9102 | 17.0 | 170 | 1.2218 | 0.5687 |
| 0.8883 | 18.0 | 180 | 1.2207 | 0.5687 |
| 0.8633 | 19.0 | 190 | 1.1933 | 0.6062 |
| 0.8557 | 20.0 | 200 | 1.1830 | 0.575 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
