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vit-Facial-Expression-Recognition
jlassi/vit-Facial-Expression-Recognition
vit-Facial-Expression-Recognition is a image classification model from jlassi. 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
- Jun 9, 2024
- Updated
- Jun 10, 2024
- SHA
- 12a02730
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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: 0.4066
- Accuracy: 0.8636
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6171 | 0.17 | 100 | 0.4053 | 0.8639 |
| 0.6082 | 0.34 | 200 | 0.4031 | 0.8638 |
| 0.611 | 0.51 | 300 | 0.4070 | 0.8611 |
| 0.5901 | 0.67 | 400 | 0.4078 | 0.8625 |
| 0.5998 | 0.84 | 500 | 0.4066 | 0.8636 |
| 0.6047 | 1.01 | 600 | 0.4137 | 0.8605 |
| 0.5927 | 1.18 | 700 | 0.4183 | 0.8563 |
| 0.5873 | 1.35 | 800 | 0.4267 | 0.8556 |
| 0.5968 | 1.52 | 900 | 0.4266 | 0.8528 |
| 0.5896 | 1.69 | 1000 | 0.4239 | 0.8556 |
| 0.5982 | 1.85 | 1100 | 0.4296 | 0.8524 |
| 0.5846 | 2.02 | 1200 | 0.4351 | 0.8519 |
| 0.5266 | 2.19 | 1300 | 0.4200 | 0.8573 |
| 0.5186 | 2.36 | 1400 | 0.4117 | 0.8598 |
| 0.5156 | 2.53 | 1500 | 0.4063 | 0.8629 |
| 0.5088 | 2.7 | 1600 | 0.4019 | 0.8646 |
| 0.4999 | 2.87 | 1700 | 0.3980 | 0.8659 |
Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- Created
- Jun 9, 2024
- Updated
- Jun 10, 2024