metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7220674282529953
beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the image_folder dataset. It achieves the following results on the evaluation set:
- Loss: 0.7881
- Accuracy: 0.7221
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: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.2307 | 1.0 | 224 | 1.0863 | 0.5874 |
| 1.0893 | 2.0 | 448 | 0.9700 | 0.6362 |
| 1.0244 | 3.0 | 672 | 0.8859 | 0.6757 |
| 1.016 | 4.0 | 896 | 0.8804 | 0.6787 |
| 0.9089 | 5.0 | 1120 | 0.8611 | 0.6897 |
| 0.8935 | 6.0 | 1344 | 0.8283 | 0.7028 |
| 0.8403 | 7.0 | 1568 | 0.8116 | 0.7102 |
| 0.8179 | 8.0 | 1792 | 0.7934 | 0.7166 |
| 0.7764 | 9.0 | 2016 | 0.7865 | 0.7208 |
| 0.771 | 10.0 | 2240 | 0.7881 | 0.7221 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1