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library_name: transformers
base_model: mo-thecreator/vit-Facial-Expression-Recognition
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-facial-expression-fatigue
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9363636363636364
---
<!-- 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-fatigue
This model is a fine-tuned version of [mo-thecreator/vit-Facial-Expression-Recognition](https://huggingface.co/mo-thecreator/vit-Facial-Expression-Recognition) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3145
- Accuracy: 0.9364
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2255 | 1.0 | 110 | 0.1784 | 0.9409 |
| 0.0959 | 2.0 | 220 | 0.2311 | 0.9364 |
| 0.0372 | 3.0 | 330 | 0.2092 | 0.9409 |
| 0.0056 | 4.0 | 440 | 0.3145 | 0.9364 |
### Framework versions
- Transformers 5.6.2
- Pytorch 2.9.1
- Datasets 4.8.4
- Tokenizers 0.22.2
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