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---
base_model: motheecreator/vit-Facial-Expression-Recognition
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-Facial-Expression-Recognition
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: None
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9148438153091649
license: apache-2.0
language:
- en
pipeline_tag: image-classification
library_name: transformers
---
<!-- 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](https://huggingface.co./motheecreator/vit-Facial-Expression-Recognition) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2606
- Accuracy: 0.9148
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6309 | 0.3328 | 100 | 0.2618 | 0.9145 |
| 0.6165 | 0.6656 | 200 | 0.2600 | 0.9150 |
| 0.6283 | 0.9983 | 300 | 0.2659 | 0.9135 |
| 0.6171 | 1.3311 | 400 | 0.2561 | 0.9174 |
| 0.6112 | 1.6639 | 500 | 0.2606 | 0.9148 |
| 0.6081 | 1.9967 | 600 | 0.2624 | 0.9137 |
| 0.5885 | 2.3295 | 700 | 0.2671 | 0.9113 |
| 0.5975 | 2.6622 | 800 | 0.2572 | 0.9156 |
| 0.6067 | 2.9950 | 900 | 0.2683 | 0.9116 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1 |