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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: 1.2024
- 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 10 | 1.3600 | 0.4938 |
| No log | 2.0 | 20 | 1.2908 | 0.4938 |
| No log | 3.0 | 30 | 1.2799 | 0.5 |
| No log | 4.0 | 40 | 1.2110 | 0.5312 |
| No log | 5.0 | 50 | 1.2178 | 0.5188 |
| No log | 6.0 | 60 | 1.2189 | 0.5188 |
| No log | 7.0 | 70 | 1.2566 | 0.5375 |
| No log | 8.0 | 80 | 1.1838 | 0.5687 |
| No log | 9.0 | 90 | 1.2730 | 0.55 |
| No log | 10.0 | 100 | 1.2329 | 0.575 |
| No log | 11.0 | 110 | 1.2224 | 0.5563 |
| No log | 12.0 | 120 | 1.2729 | 0.5563 |
| No log | 13.0 | 130 | 1.2678 | 0.5687 |
| No log | 14.0 | 140 | 1.2423 | 0.5687 |
| No log | 15.0 | 150 | 1.1704 | 0.6312 |
| No log | 16.0 | 160 | 1.2925 | 0.5625 |
| No log | 17.0 | 170 | 1.3557 | 0.5312 |
| No log | 18.0 | 180 | 1.2951 | 0.5687 |
| No log | 19.0 | 190 | 1.2594 | 0.5625 |
| No log | 20.0 | 200 | 1.2463 | 0.5687 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
