metadata
license: apache-2.0
base_model: motheecreator/vit-Facial-Expression-Recognition
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: vit-Facial-Expression-Recognition
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7444126074498567
vit-Facial-Expression-Recognition
This model is a fine-tuned version of motheecreator/vit-Facial-Expression-Recognition on the image_folder dataset. It achieves the following results on the evaluation set:
- Loss: 0.7038
- Accuracy: 0.7444
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 0.7175 | 1.0 | 654 | 0.7309 | 0.7081 |
| 0.6952 | 2.0 | 1308 | 0.7379 | 0.6931 |
| 0.5041 | 3.0 | 1962 | 0.7444 | 0.7038 |
| 0.2461 | 4.0 | 2617 | 0.7393 | 0.7843 |
| 0.1846 | 5.0 | 3270 | 0.7391 | 0.8219 |
| 0.276 | 6.0 | 3924 | 0.8876 | 0.7335 |
| 0.2217 | 7.0 | 4578 | 0.9752 | 0.7255 |
| 0.0646 | 8.0 | 5232 | 1.0957 | 0.7263 |
| 0.063 | 9.0 | 5887 | 1.1335 | 0.7263 |
| 0.0562 | 10.0 | 6540 | 1.1663 | 0.7307 |
Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0