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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: 1.9455
- Accuracy: 0.3
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: 1e-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: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 10 | 2.0636 | 0.1187 |
| No log | 2.0 | 20 | 2.0568 | 0.1437 |
| No log | 3.0 | 30 | 2.0321 | 0.1812 |
| No log | 4.0 | 40 | 2.0247 | 0.2 |
| No log | 5.0 | 50 | 1.9975 | 0.3125 |
| No log | 6.0 | 60 | 1.9793 | 0.2875 |
| No log | 7.0 | 70 | 1.9746 | 0.275 |
| No log | 8.0 | 80 | 1.9530 | 0.3063 |
| No log | 9.0 | 90 | 1.9487 | 0.3438 |
| No log | 10.0 | 100 | 1.9513 | 0.2812 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
