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base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
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: 0.65
- name: F1
type: f1
value: 0.6231481481481482
---
<!-- 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: 1.1136
- Accuracy: 0.65
- F1: 0.6231
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 45
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.9172 | 1.0 | 43 | 1.5751 | 0.4333 | 0.3263 |
| 1.4505 | 2.0 | 86 | 1.3041 | 0.5333 | 0.4651 |
| 1.1121 | 3.0 | 129 | 1.2902 | 0.4833 | 0.4684 |
| 0.8491 | 4.0 | 172 | 1.2309 | 0.5167 | 0.4916 |
| 0.6168 | 5.0 | 215 | 1.2573 | 0.5583 | 0.5310 |
| 0.3953 | 6.0 | 258 | 1.1502 | 0.575 | 0.5401 |
| 0.3048 | 7.0 | 301 | 1.1136 | 0.65 | 0.6231 |
| 0.1875 | 8.0 | 344 | 1.4224 | 0.5667 | 0.5598 |
| 0.1277 | 9.0 | 387 | 1.3467 | 0.6167 | 0.6011 |
| 0.1123 | 10.0 | 430 | 1.5838 | 0.5833 | 0.5657 |
| 0.1123 | 11.0 | 473 | 1.5063 | 0.5833 | 0.5550 |
| 0.0694 | 12.0 | 516 | 1.7733 | 0.55 | 0.5320 |
| 0.0499 | 13.0 | 559 | 1.6329 | 0.5833 | 0.5536 |
| 0.0367 | 14.0 | 602 | 1.6878 | 0.5833 | 0.5685 |
| 0.0291 | 15.0 | 645 | 1.6855 | 0.575 | 0.5392 |
| 0.0284 | 16.0 | 688 | 1.7869 | 0.6083 | 0.5880 |
| 0.0316 | 17.0 | 731 | 1.5831 | 0.5917 | 0.5670 |
| 0.0273 | 18.0 | 774 | 1.5933 | 0.625 | 0.5984 |
| 0.0234 | 19.0 | 817 | 1.7830 | 0.5833 | 0.5652 |
| 0.0194 | 20.0 | 860 | 1.6804 | 0.6083 | 0.5878 |
| 0.0214 | 21.0 | 903 | 1.5962 | 0.6 | 0.5701 |
| 0.0204 | 22.0 | 946 | 1.5684 | 0.625 | 0.5992 |
| 0.0178 | 23.0 | 989 | 1.5924 | 0.625 | 0.5992 |
| 0.0173 | 24.0 | 1032 | 1.6228 | 0.6167 | 0.5933 |
| 0.016 | 25.0 | 1075 | 1.6177 | 0.6333 | 0.6073 |
| 0.016 | 26.0 | 1118 | 1.6268 | 0.625 | 0.6009 |
| 0.016 | 27.0 | 1161 | 1.6387 | 0.625 | 0.6009 |
| 0.0159 | 28.0 | 1204 | 1.6403 | 0.625 | 0.6009 |
| 0.0162 | 29.0 | 1247 | 1.6409 | 0.625 | 0.6009 |
| 0.018 | 30.0 | 1290 | 1.6412 | 0.625 | 0.6009 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
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