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---
library_name: transformers
base_model: carlosleao/vit-Facial-Expression-Recognition
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-Facial-Expression-Recognition
results: []
---
<!-- 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 [carlosleao/vit-Facial-Expression-Recognition](https://huggingface.co/carlosleao/vit-Facial-Expression-Recognition) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2687
- Accuracy: 0.4177
## 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.9372 | 0.8959 | 100 | 1.5720 | 0.4417 |
| 0.9147 | 1.7917 | 200 | 1.6084 | 0.4364 |
| 0.8393 | 2.6876 | 300 | 1.7268 | 0.4169 |
| 0.7882 | 3.5834 | 400 | 1.7604 | 0.4227 |
| 0.6916 | 4.4793 | 500 | 1.8619 | 0.4124 |
| 0.6367 | 5.3751 | 600 | 1.9493 | 0.4261 |
| 0.5848 | 6.2710 | 700 | 2.0511 | 0.4046 |
| 0.5183 | 7.1669 | 800 | 2.1316 | 0.4230 |
| 0.4788 | 8.0627 | 900 | 2.2210 | 0.4026 |
| 0.4586 | 8.9586 | 1000 | 2.2687 | 0.4177 |
| 0.4079 | 9.8544 | 1100 | 2.4038 | 0.3747 |
| 0.3797 | 10.7503 | 1200 | 2.3664 | 0.4046 |
| 0.2957 | 11.6461 | 1300 | 2.4534 | 0.4068 |
| 0.2622 | 12.5420 | 1400 | 2.5413 | 0.3956 |
| 0.2202 | 13.4378 | 1500 | 2.5601 | 0.4127 |
| 0.2112 | 14.3337 | 1600 | 2.6560 | 0.3920 |
| 0.1769 | 15.2296 | 1700 | 2.8006 | 0.3909 |
| 0.161 | 16.1254 | 1800 | 2.8011 | 0.3928 |
| 0.155 | 17.0213 | 1900 | 2.9518 | 0.3856 |
| 0.1309 | 17.9171 | 2000 | 2.9363 | 0.3727 |
| 0.1001 | 18.8130 | 2100 | 2.9187 | 0.3998 |
| 0.0816 | 19.7088 | 2200 | 3.0563 | 0.3842 |
| 0.0672 | 20.6047 | 2300 | 2.9358 | 0.4205 |
| 0.0567 | 21.5006 | 2400 | 3.1118 | 0.3970 |
| 0.0524 | 22.3964 | 2500 | 3.2147 | 0.4054 |
| 0.0413 | 23.2923 | 2600 | 3.1928 | 0.3951 |
| 0.0368 | 24.1881 | 2700 | 3.1599 | 0.4141 |
| 0.0275 | 25.0840 | 2800 | 3.1720 | 0.4166 |
| 0.029 | 25.9798 | 2900 | 3.1924 | 0.4012 |
| 0.0231 | 26.8757 | 3000 | 3.2031 | 0.4088 |
| 0.0226 | 27.7716 | 3100 | 3.2125 | 0.4113 |
| 0.0205 | 28.6674 | 3200 | 3.2122 | 0.4118 |
| 0.0197 | 29.5633 | 3300 | 3.2126 | 0.4116 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|