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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: visual_emotion_classification_vit_base_finetunned
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: 0.51875
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# visual_emotion_classification_vit_base_finetunned
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2429
- Accuracy: 0.5188
## 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: 1e-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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.026 | 1.25 | 100 | 2.0071 | 0.275 |
| 1.8882 | 2.5 | 200 | 1.8921 | 0.3625 |
| 1.7186 | 3.75 | 300 | 1.7326 | 0.4188 |
| 1.5892 | 5.0 | 400 | 1.6242 | 0.475 |
| 1.4942 | 6.25 | 500 | 1.5443 | 0.5125 |
| 1.3825 | 7.5 | 600 | 1.4763 | 0.5062 |
| 1.3084 | 8.75 | 700 | 1.4554 | 0.4938 |
| 1.2388 | 10.0 | 800 | 1.4057 | 0.525 |
| 1.1519 | 11.25 | 900 | 1.3756 | 0.4938 |
| 1.1054 | 12.5 | 1000 | 1.3604 | 0.4875 |
| 1.0605 | 13.75 | 1100 | 1.3597 | 0.4938 |
| 1.016 | 15.0 | 1200 | 1.3370 | 0.4938 |
| 0.9601 | 16.25 | 1300 | 1.2981 | 0.4938 |
| 0.8445 | 17.5 | 1400 | 1.2420 | 0.5563 |
| 0.8514 | 18.75 | 1500 | 1.2485 | 0.5625 |
| 0.7899 | 20.0 | 1600 | 1.2861 | 0.4875 |
| 0.7459 | 21.25 | 1700 | 1.2860 | 0.4875 |
| 0.6917 | 22.5 | 1800 | 1.2335 | 0.5813 |
| 0.6864 | 23.75 | 1900 | 1.2726 | 0.5437 |
| 0.6414 | 25.0 | 2000 | 1.2215 | 0.5375 |
| 0.5583 | 26.25 | 2100 | 1.2756 | 0.5312 |
| 0.597 | 27.5 | 2200 | 1.2314 | 0.5375 |
| 0.5654 | 28.75 | 2300 | 1.3791 | 0.5125 |
| 0.5798 | 30.0 | 2400 | 1.1890 | 0.5687 |
| 0.5247 | 31.25 | 2500 | 1.2440 | 0.5687 |
| 0.5099 | 32.5 | 2600 | 1.2787 | 0.5625 |
| 0.496 | 33.75 | 2700 | 1.2628 | 0.55 |
| 0.479 | 35.0 | 2800 | 1.3420 | 0.4875 |
| 0.4685 | 36.25 | 2900 | 1.2817 | 0.5563 |
| 0.4375 | 37.5 | 3000 | 1.3122 | 0.525 |
| 0.4314 | 38.75 | 3100 | 1.1791 | 0.5563 |
| 0.4174 | 40.0 | 3200 | 1.2322 | 0.55 |
| 0.4019 | 41.25 | 3300 | 1.3871 | 0.5125 |
| 0.3738 | 42.5 | 3400 | 1.2854 | 0.5312 |
| 0.3938 | 43.75 | 3500 | 1.3057 | 0.5375 |
| 0.369 | 45.0 | 3600 | 1.2792 | 0.5437 |
| 0.3768 | 46.25 | 3700 | 1.2761 | 0.5625 |
| 0.3202 | 47.5 | 3800 | 1.2704 | 0.5375 |
| 0.3859 | 48.75 | 3900 | 1.2746 | 0.5312 |
| 0.3689 | 50.0 | 4000 | 1.3306 | 0.5563 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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