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vit-Facial-Expression-Recognition
vkushwahaa/vit-Facial-Expression-Recognition
vit-Facial-Expression-Recognition is a image classification model from vkushwahaa. 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
- Mar 11, 2025
- Updated
- Apr 19, 2025
- SHA
- f6871294
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: 0.5369
- Accuracy: 0.8095
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 |
|---|---|---|---|---|
| 0.8526 | 0.2077 | 100 | 0.6490 | 0.7761 |
| 0.8116 | 0.4155 | 200 | 0.6106 | 0.7860 |
| 0.7759 | 0.6232 | 300 | 0.5918 | 0.7908 |
| 0.7708 | 0.8310 | 400 | 0.5883 | 0.7904 |
| 0.75 | 1.0374 | 500 | 0.5749 | 0.7971 |
| 0.7496 | 1.2451 | 600 | 0.5737 | 0.7976 |
| 0.7601 | 1.4529 | 700 | 0.5698 | 0.7971 |
| 0.7516 | 1.6606 | 800 | 0.5692 | 0.7991 |
| 0.7359 | 1.8683 | 900 | 0.5740 | 0.7947 |
| 0.6968 | 2.0748 | 1000 | 0.5738 | 0.7953 |
| 0.6854 | 2.2825 | 1100 | 0.5617 | 0.7980 |
| 0.6992 | 2.4903 | 1200 | 0.5566 | 0.8030 |
| 0.6609 | 2.6980 | 1300 | 0.5495 | 0.8035 |
| 0.664 | 2.9057 | 1400 | 0.5370 | 0.8101 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- Created
- Mar 11, 2025
- Updated
- Apr 19, 2025