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---
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: 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: 0.55625
---
<!-- 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_classification
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: 1.2963
- Accuracy: 0.5563
## 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: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0771 | 1.0 | 10 | 2.0698 | 0.1375 |
| 2.0613 | 2.0 | 20 | 2.0368 | 0.2875 |
| 2.0214 | 3.0 | 30 | 2.0010 | 0.2625 |
| 1.9314 | 4.0 | 40 | 1.8913 | 0.3 |
| 1.785 | 5.0 | 50 | 1.7270 | 0.375 |
| 1.6343 | 6.0 | 60 | 1.6009 | 0.4313 |
| 1.5327 | 7.0 | 70 | 1.5766 | 0.3937 |
| 1.452 | 8.0 | 80 | 1.4714 | 0.475 |
| 1.38 | 9.0 | 90 | 1.4570 | 0.4688 |
| 1.3061 | 10.0 | 100 | 1.4357 | 0.4688 |
| 1.2331 | 11.0 | 110 | 1.3691 | 0.4938 |
| 1.1784 | 12.0 | 120 | 1.3377 | 0.4813 |
| 1.1049 | 13.0 | 130 | 1.2982 | 0.5625 |
| 1.0938 | 14.0 | 140 | 1.2847 | 0.5188 |
| 1.0191 | 15.0 | 150 | 1.2630 | 0.575 |
| 0.9665 | 16.0 | 160 | 1.3427 | 0.4938 |
| 0.9028 | 17.0 | 170 | 1.3189 | 0.525 |
| 0.886 | 18.0 | 180 | 1.2599 | 0.5312 |
| 0.8272 | 19.0 | 190 | 1.3148 | 0.525 |
| 0.7923 | 20.0 | 200 | 1.2634 | 0.55 |
| 0.8033 | 21.0 | 210 | 1.2664 | 0.5625 |
| 0.724 | 22.0 | 220 | 1.2286 | 0.525 |
| 0.6966 | 23.0 | 230 | 1.3408 | 0.5375 |
| 0.6722 | 24.0 | 240 | 1.3032 | 0.5062 |
| 0.6816 | 25.0 | 250 | 1.3318 | 0.5062 |
| 0.6162 | 26.0 | 260 | 1.3775 | 0.4938 |
| 0.6099 | 27.0 | 270 | 1.2903 | 0.5437 |
| 0.5786 | 28.0 | 280 | 1.2361 | 0.6 |
| 0.5931 | 29.0 | 290 | 1.2998 | 0.5312 |
| 0.5849 | 30.0 | 300 | 1.3221 | 0.5062 |
| 0.5606 | 31.0 | 310 | 1.2756 | 0.5125 |
| 0.5561 | 32.0 | 320 | 1.3732 | 0.4813 |
| 0.547 | 33.0 | 330 | 1.3308 | 0.5375 |
| 0.5405 | 34.0 | 340 | 1.3506 | 0.5062 |
| 0.5419 | 35.0 | 350 | 1.2487 | 0.5625 |
| 0.5168 | 36.0 | 360 | 1.2269 | 0.525 |
| 0.5361 | 37.0 | 370 | 1.2993 | 0.55 |
| 0.5375 | 38.0 | 380 | 1.2806 | 0.575 |
| 0.5235 | 39.0 | 390 | 1.3404 | 0.5188 |
| 0.5318 | 40.0 | 400 | 1.3315 | 0.4938 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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