metadata
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: 0.9176650877681806
vit-Facial-Expression-Recognition
This model is a fine-tuned version of motheecreator/vit-Facial-Expression-Recognition on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2631
- Accuracy: 0.9177
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 |
|---|---|---|---|---|
| 0.5315 | 0.8909 | 100 | 0.2603 | 0.9184 |
| 0.5202 | 1.7817 | 200 | 0.2583 | 0.9181 |
| 0.4912 | 2.6726 | 300 | 0.2609 | 0.9171 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1