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emotion_classification_v1
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: 1.1905
- Accuracy: 0.575
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: 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: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 10 | 2.0278 | 0.2437 |
| No log | 2.0 | 20 | 1.8875 | 0.3875 |
| No log | 3.0 | 30 | 1.6890 | 0.4313 |
| No log | 4.0 | 40 | 1.5484 | 0.5 |
| No log | 5.0 | 50 | 1.4799 | 0.5125 |
| No log | 6.0 | 60 | 1.4148 | 0.5375 |
| No log | 7.0 | 70 | 1.3529 | 0.5375 |
| No log | 8.0 | 80 | 1.3120 | 0.5312 |
| No log | 9.0 | 90 | 1.2790 | 0.5813 |
| No log | 10.0 | 100 | 1.2498 | 0.575 |
| No log | 11.0 | 110 | 1.2610 | 0.525 |
| No log | 12.0 | 120 | 1.1896 | 0.5938 |
| No log | 13.0 | 130 | 1.2251 | 0.5312 |
| No log | 14.0 | 140 | 1.2019 | 0.575 |
| No log | 15.0 | 150 | 1.1797 | 0.5563 |
| No log | 16.0 | 160 | 1.2484 | 0.5437 |
| No log | 17.0 | 170 | 1.1766 | 0.5875 |
| No log | 18.0 | 180 | 1.2401 | 0.4938 |
| No log | 19.0 | 190 | 1.1977 | 0.5312 |
| No log | 20.0 | 200 | 1.1839 | 0.5875 |
| No log | 21.0 | 210 | 1.2028 | 0.5687 |
| No log | 22.0 | 220 | 1.2048 | 0.5625 |
| No log | 23.0 | 230 | 1.2637 | 0.5375 |
| No log | 24.0 | 240 | 1.2371 | 0.5375 |
| No log | 25.0 | 250 | 1.2777 | 0.5687 |
| No log | 26.0 | 260 | 1.2544 | 0.525 |
| No log | 27.0 | 270 | 1.2104 | 0.5625 |
| No log | 28.0 | 280 | 1.1372 | 0.5938 |
| No log | 29.0 | 290 | 1.2405 | 0.575 |
| No log | 30.0 | 300 | 1.1624 | 0.6062 |
| No log | 31.0 | 310 | 1.2376 | 0.5875 |
| No log | 32.0 | 320 | 1.1794 | 0.5875 |
| No log | 33.0 | 330 | 1.2156 | 0.5563 |
| No log | 34.0 | 340 | 1.1725 | 0.55 |
| No log | 35.0 | 350 | 1.2394 | 0.55 |
| No log | 36.0 | 360 | 1.1886 | 0.5938 |
| No log | 37.0 | 370 | 1.1760 | 0.6188 |
| No log | 38.0 | 380 | 1.2757 | 0.525 |
| No log | 39.0 | 390 | 1.1703 | 0.6062 |
| No log | 40.0 | 400 | 1.2734 | 0.575 |
| No log | 41.0 | 410 | 1.2265 | 0.5563 |
| No log | 42.0 | 420 | 1.2651 | 0.5687 |
| No log | 43.0 | 430 | 1.2419 | 0.5813 |
| No log | 44.0 | 440 | 1.1871 | 0.6 |
| No log | 45.0 | 450 | 1.2542 | 0.575 |
| No log | 46.0 | 460 | 1.1910 | 0.5813 |
| No log | 47.0 | 470 | 1.1990 | 0.6 |
| No log | 48.0 | 480 | 1.2097 | 0.5813 |
| No log | 49.0 | 490 | 1.2226 | 0.5875 |
| 0.699 | 50.0 | 500 | 1.2793 | 0.5375 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
