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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: 1.2445
- Accuracy: 0.5312
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: 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 | 10 | 1.9385 | 0.325 |
| No log | 2.0 | 20 | 1.7153 | 0.4188 |
| No log | 3.0 | 30 | 1.5905 | 0.3937 |
| No log | 4.0 | 40 | 1.4706 | 0.4625 |
| No log | 5.0 | 50 | 1.4078 | 0.5062 |
| No log | 6.0 | 60 | 1.3739 | 0.4813 |
| No log | 7.0 | 70 | 1.3108 | 0.5125 |
| No log | 8.0 | 80 | 1.2874 | 0.5312 |
| No log | 9.0 | 90 | 1.2810 | 0.5312 |
| No log | 10.0 | 100 | 1.2754 | 0.5437 |
| No log | 11.0 | 110 | 1.2380 | 0.5563 |
| No log | 12.0 | 120 | 1.1721 | 0.6125 |
| No log | 13.0 | 130 | 1.2242 | 0.5875 |
| No log | 14.0 | 140 | 1.2530 | 0.525 |
| No log | 15.0 | 150 | 1.2610 | 0.575 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
