metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-emotion-classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.56875
vit-emotion-classification
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.3912
- Accuracy: 0.5687
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.058 | 1.0 | 80 | 1.9682 | 0.3063 |
| 1.7534 | 2.0 | 160 | 1.7016 | 0.3875 |
| 1.5632 | 3.0 | 240 | 1.5568 | 0.4688 |
| 1.2999 | 4.0 | 320 | 1.4694 | 0.5437 |
| 1.1246 | 5.0 | 400 | 1.3912 | 0.5687 |
| 0.9904 | 6.0 | 480 | 1.3551 | 0.5625 |
| 0.8557 | 7.0 | 560 | 1.3209 | 0.5625 |
| 0.7612 | 8.0 | 640 | 1.3006 | 0.5625 |
| 0.6658 | 9.0 | 720 | 1.2911 | 0.5687 |
| 0.6531 | 10.0 | 800 | 1.2854 | 0.5563 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1