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vit-Facial-Expression-Recognition
rbenrejeb/vit-Facial-Expression-Recognition
vit-Facial-Expression-Recognition is a image classification model from rbenrejeb. Use it when you need a label for an image. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- Model type
- vit
- Access
- Public
- Created
- Dec 18, 2024
- Updated
- Dec 18, 2024
- SHA
- 4c242350
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
vit-Facial-Expression-Recognition
This model is a fine-tuned version of motheecreator/vit-Facial-Expression-Recognition on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3720
- Accuracy: 0.8732
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5752 | 0.2164 | 100 | 0.3737 | 0.8740 |
| 0.568 | 0.4328 | 200 | 0.3759 | 0.8720 |
| 0.551 | 0.6492 | 300 | 0.3722 | 0.8734 |
| 0.5604 | 0.8656 | 400 | 0.3747 | 0.8733 |
| 0.5391 | 1.0820 | 500 | 0.3720 | 0.8732 |
| 0.5751 | 1.2984 | 600 | 0.3761 | 0.8718 |
| 0.5678 | 1.5147 | 700 | 0.3824 | 0.8691 |
| 0.5493 | 1.7311 | 800 | 0.3870 | 0.8672 |
| 0.5766 | 1.9475 | 900 | 0.3942 | 0.8629 |
| 0.5301 | 2.1639 | 1000 | 0.3947 | 0.8639 |
| 0.5092 | 2.3803 | 1100 | 0.3896 | 0.8656 |
| 0.5164 | 2.5967 | 1200 | 0.3778 | 0.8703 |
| 0.4971 | 2.8131 | 1300 | 0.3731 | 0.8730 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- Created
- Dec 18, 2024
- Updated
- Dec 18, 2024