emotion_recognition
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: 1.1376
- Accuracy: 0.6062
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 20 | 1.3456 | 0.4813 |
| No log | 2.0 | 40 | 1.3147 | 0.5188 |
| No log | 3.0 | 60 | 1.2345 | 0.5563 |
| No log | 4.0 | 80 | 1.2281 | 0.5625 |
| No log | 5.0 | 100 | 1.1851 | 0.5687 |
| No log | 6.0 | 120 | 1.1911 | 0.5563 |
| No log | 7.0 | 140 | 1.1834 | 0.5813 |
| No log | 8.0 | 160 | 1.1682 | 0.5875 |
| No log | 9.0 | 180 | 1.2359 | 0.55 |
| No log | 10.0 | 200 | 1.1850 | 0.5563 |
| No log | 11.0 | 220 | 1.1877 | 0.5687 |
| No log | 12.0 | 240 | 1.1546 | 0.5687 |
| No log | 13.0 | 260 | 1.1694 | 0.5813 |
| No log | 14.0 | 280 | 1.2401 | 0.5875 |
| No log | 15.0 | 300 | 1.1899 | 0.575 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1