metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: 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: 0.46875
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: 1.4050
- Accuracy: 0.4688
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|---|---|---|---|---|
| 1.8187 | 1.0 | 10 | 1.8406 | 0.3063 |
| 1.6795 | 2.0 | 20 | 1.6701 | 0.3688 |
| 1.5506 | 3.0 | 30 | 1.5578 | 0.45 |
| 1.4417 | 4.0 | 40 | 1.5077 | 0.4875 |
| 1.3707 | 5.0 | 50 | 1.4297 | 0.5062 |
| 1.3167 | 6.0 | 60 | 1.4157 | 0.4938 |
| 1.267 | 7.0 | 70 | 1.3779 | 0.525 |
| 1.2197 | 8.0 | 80 | 1.3784 | 0.5 |
| 1.191 | 9.0 | 90 | 1.3701 | 0.5188 |
| 1.1649 | 10.0 | 100 | 1.3611 | 0.4938 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3