| | --- |
| | 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 |
| | |
