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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: emotion_recognition
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: 0.6125
---
<!-- 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. -->
# emotion_recognition
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: 1.2014
- Accuracy: 0.6125
## 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: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0842 | 1.0 | 10 | 2.0668 | 0.175 |
| 2.039 | 2.0 | 20 | 2.0070 | 0.2875 |
| 1.9285 | 3.0 | 30 | 1.8789 | 0.4062 |
| 1.7699 | 4.0 | 40 | 1.6942 | 0.425 |
| 1.6135 | 5.0 | 50 | 1.5758 | 0.4313 |
| 1.5056 | 6.0 | 60 | 1.4884 | 0.55 |
| 1.3896 | 7.0 | 70 | 1.3999 | 0.5437 |
| 1.2804 | 8.0 | 80 | 1.3563 | 0.5437 |
| 1.2043 | 9.0 | 90 | 1.3244 | 0.55 |
| 1.1231 | 10.0 | 100 | 1.2775 | 0.6062 |
| 1.0652 | 11.0 | 110 | 1.2567 | 0.575 |
| 1.0005 | 12.0 | 120 | 1.2833 | 0.5563 |
| 0.9878 | 13.0 | 130 | 1.2277 | 0.5687 |
| 0.9714 | 14.0 | 140 | 1.2557 | 0.5563 |
| 0.9057 | 15.0 | 150 | 1.2187 | 0.6125 |
| 0.8854 | 16.0 | 160 | 1.2612 | 0.5437 |
| 0.8478 | 17.0 | 170 | 1.2450 | 0.5437 |
| 0.8601 | 18.0 | 180 | 1.2456 | 0.5375 |
| 0.8498 | 19.0 | 190 | 1.2413 | 0.5875 |
| 0.8775 | 20.0 | 200 | 1.1928 | 0.6 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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