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emotion_model
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: 1.3497
- 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: 0.0001
- 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: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.0823 | 1.0 | 10 | 2.0560 | 0.1625 |
| 2.0479 | 2.0 | 20 | 2.0218 | 0.2812 |
| 1.9636 | 3.0 | 30 | 1.8882 | 0.4062 |
| 1.7902 | 4.0 | 40 | 1.6881 | 0.4313 |
| 1.5792 | 5.0 | 50 | 1.6159 | 0.3688 |
| 1.4429 | 6.0 | 60 | 1.3871 | 0.5687 |
| 1.2854 | 7.0 | 70 | 1.2973 | 0.5437 |
| 1.1487 | 8.0 | 80 | 1.2303 | 0.6 |
| 1.0374 | 9.0 | 90 | 1.2661 | 0.5375 |
| 0.9584 | 10.0 | 100 | 1.1662 | 0.5563 |
| 0.8108 | 11.0 | 110 | 1.2135 | 0.5312 |
| 0.7402 | 12.0 | 120 | 1.2117 | 0.5813 |
| 0.6349 | 13.0 | 130 | 1.1176 | 0.6062 |
| 0.5674 | 14.0 | 140 | 1.1794 | 0.575 |
| 0.5103 | 15.0 | 150 | 1.0948 | 0.6375 |
| 0.4826 | 16.0 | 160 | 1.1833 | 0.5875 |
| 0.4128 | 17.0 | 170 | 1.2601 | 0.5375 |
| 0.3664 | 18.0 | 180 | 1.3378 | 0.55 |
| 0.3112 | 19.0 | 190 | 1.2789 | 0.5437 |
| 0.335 | 20.0 | 200 | 1.2913 | 0.5625 |
| 0.3261 | 21.0 | 210 | 1.1114 | 0.6 |
| 0.3443 | 22.0 | 220 | 1.2177 | 0.5938 |
| 0.2642 | 23.0 | 230 | 1.2299 | 0.5938 |
| 0.2895 | 24.0 | 240 | 1.2339 | 0.5813 |
| 0.266 | 25.0 | 250 | 1.2384 | 0.5875 |
| 0.2725 | 26.0 | 260 | 1.2100 | 0.6062 |
| 0.2725 | 27.0 | 270 | 1.3073 | 0.575 |
| 0.2637 | 28.0 | 280 | 1.3019 | 0.5875 |
| 0.2561 | 29.0 | 290 | 1.3597 | 0.5437 |
| 0.2375 | 30.0 | 300 | 1.3404 | 0.5563 |
| 0.2188 | 31.0 | 310 | 1.2922 | 0.5813 |
| 0.2141 | 32.0 | 320 | 1.3778 | 0.5312 |
| 0.198 | 33.0 | 330 | 1.3473 | 0.5875 |
| 0.1805 | 34.0 | 340 | 1.3984 | 0.5437 |
| 0.1888 | 35.0 | 350 | 1.3508 | 0.5813 |
| 0.1867 | 36.0 | 360 | 1.3531 | 0.575 |
| 0.1596 | 37.0 | 370 | 1.5846 | 0.4875 |
| 0.1564 | 38.0 | 380 | 1.3380 | 0.5687 |
| 0.1719 | 39.0 | 390 | 1.5206 | 0.5312 |
| 0.1678 | 40.0 | 400 | 1.2929 | 0.5875 |
| 0.136 | 41.0 | 410 | 1.5031 | 0.55 |
| 0.1602 | 42.0 | 420 | 1.3855 | 0.5625 |
| 0.174 | 43.0 | 430 | 1.4385 | 0.5875 |
| 0.179 | 44.0 | 440 | 1.3153 | 0.575 |
| 0.1284 | 45.0 | 450 | 1.4295 | 0.5875 |
| 0.1419 | 46.0 | 460 | 1.4126 | 0.575 |
| 0.1425 | 47.0 | 470 | 1.3760 | 0.5687 |
| 0.1602 | 48.0 | 480 | 1.4374 | 0.5875 |
| 0.1473 | 49.0 | 490 | 1.3126 | 0.5813 |
| 0.153 | 50.0 | 500 | 1.3497 | 0.6 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
