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: 1.2493
- Accuracy: 0.5687
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 |
|---|---|---|---|---|
| 2.0679 | 1.0 | 10 | 2.0574 | 0.175 |
| 2.0366 | 2.0 | 20 | 2.0083 | 0.2812 |
| 1.9469 | 3.0 | 30 | 1.9119 | 0.35 |
| 1.8166 | 4.0 | 40 | 1.7702 | 0.4125 |
| 1.6821 | 5.0 | 50 | 1.6176 | 0.45 |
| 1.5587 | 6.0 | 60 | 1.5747 | 0.425 |
| 1.4703 | 7.0 | 70 | 1.4444 | 0.5375 |
| 1.4032 | 8.0 | 80 | 1.4226 | 0.5312 |
| 1.3367 | 9.0 | 90 | 1.3937 | 0.5188 |
| 1.2889 | 10.0 | 100 | 1.3186 | 0.5375 |
| 1.2136 | 11.0 | 110 | 1.3313 | 0.55 |
| 1.1745 | 12.0 | 120 | 1.3027 | 0.5312 |
| 1.1477 | 13.0 | 130 | 1.3004 | 0.5375 |
| 1.1414 | 14.0 | 140 | 1.2442 | 0.55 |
| 1.1202 | 15.0 | 150 | 1.2957 | 0.5062 |
| 1.0923 | 16.0 | 160 | 1.3045 | 0.5125 |
| 1.0765 | 17.0 | 170 | 1.2533 | 0.5563 |
| 1.0678 | 18.0 | 180 | 1.2392 | 0.5437 |
| 1.0837 | 19.0 | 190 | 1.2750 | 0.5375 |
| 1.0562 | 20.0 | 200 | 1.2275 | 0.5625 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Base model
google/vit-base-patch16-224-in21k