| --- |
| license: apache-2.0 |
| base_model: microsoft/beit-large-patch16-224-pt22k-ft22k |
| tags: |
| - generated_from_trainer |
| metrics: |
| - pearsonr |
| - r_squared |
| model-index: |
| - name: motes_mtci_microsoft-beit-large-patch16-224-pt22k-ft22k |
| results: [] |
| --- |
| |
| # Ocsai-D Web |
|
|
| This model is a trained model for scoring creativity - specifically figural (drawing-based) originality scoring. It is a fine-tuned version of [beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224-pt22k-ft22k). |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0055 |
| - Mse: 0.0055 |
| - Pearsonr: 0.8745 |
| - R2: 0.7224 |
| - Rmse: 0.0745 |
|
|
| It can be tried at <https://openscoring.du.edu/draw>. |
|
|
| ## Model description |
|
|
| See the pre-print: |
|
|
| Acar, S.^, Organisciak, P.^, & Dumas, D. (2023). Automated Scoring of Figural Tests of Creativity with Computer Vision. http://dx.doi.org/10.13140/RG.2.2.26865.25444 |
|
|
| *^Authors contributed equally.* |
|
|
|
|
| ## Intended uses & limitations |
|
|
| This model judges the originality of figural drawings. There are some limitations. |
|
|
| First, there is a confound with elaboration - drawing more leads - partially - to higher originality. |
|
|
| Secondly, the training is specific to one test, and mileage may vary on other images. |
|
|
| ## Training and evaluation data |
|
|
| This is trained on the Multi-Trial Creative Ideation task (MTCI; [Barbot 2018](https://pubmed.ncbi.nlm.nih.gov/30618952/)), with the [data](https://osf.io/kqn9v/) from Patterson et al. ([2023](https://doi.org/10.31234/osf.io/t63dm)). |
|
|
| For Ocsai-Web, we used a larger training split, 95%, and bound zero-originality images to zero. |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 20 |
| - eval_batch_size: 20 |
| - seed: 42 |
| - gradient_accumulation_steps: 8 |
| - total_train_batch_size: 160 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.2 |
| - num_epochs: 12 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Mse | Pearsonr | R2 | Rmse | |
| |:-------------:|:------:|:----:|:---------------:|:------:|:--------:|:-------:|:------:| |
| | 0.0728 | 0.3992 | 25 | 0.0141 | 0.0141 | 0.6466 | -0.0091 | 0.1189 | |
| | 0.0137 | 0.7984 | 50 | 0.0094 | 0.0094 | 0.7812 | 0.0650 | 0.0968 | |
| | 0.0153 | 1.1976 | 75 | 0.0118 | 0.0118 | 0.8137 | 0.1092 | 0.1087 | |
| | 0.0155 | 1.5968 | 100 | 0.0168 | 0.0168 | 0.8303 | -0.3131 | 0.1295 | |
| | 0.0157 | 1.9960 | 125 | 0.0080 | 0.0080 | 0.8347 | 0.2944 | 0.0893 | |
| | 0.0087 | 2.3952 | 150 | 0.0068 | 0.0068 | 0.8488 | 0.5258 | 0.0827 | |
| | 0.0078 | 2.7944 | 175 | 0.0093 | 0.0093 | 0.8541 | 0.3130 | 0.0963 | |
| | 0.0079 | 3.1936 | 200 | 0.0092 | 0.0092 | 0.8604 | 0.3562 | 0.0960 | |
| | 0.0073 | 3.5928 | 225 | 0.0076 | 0.0076 | 0.8684 | 0.5507 | 0.0871 | |
| | 0.007 | 3.9920 | 250 | 0.0082 | 0.0082 | 0.8662 | 0.5539 | 0.0904 | |
| | 0.0055 | 4.3912 | 275 | 0.0055 | 0.0055 | 0.8727 | 0.6912 | 0.0744 | |
| | 0.0042 | 4.7904 | 300 | 0.0060 | 0.0060 | 0.8737 | 0.6844 | 0.0773 | |
| | 0.0037 | 5.1896 | 325 | 0.0061 | 0.0061 | 0.8702 | 0.6496 | 0.0781 | |
| | 0.0034 | 5.5888 | 350 | 0.0061 | 0.0061 | 0.8707 | 0.6426 | 0.0781 | |
| | 0.0031 | 5.9880 | 375 | 0.0057 | 0.0057 | 0.8717 | 0.7266 | 0.0757 | |
| | 0.0023 | 6.3872 | 400 | 0.0056 | 0.0056 | 0.8716 | 0.7084 | 0.0749 | |
| | 0.002 | 6.7864 | 425 | 0.0056 | 0.0056 | 0.8708 | 0.6710 | 0.0745 | |
| | 0.0018 | 7.1856 | 450 | 0.0055 | 0.0055 | 0.8745 | 0.7224 | 0.0745 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.40.0 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.19.0 |
| - Tokenizers 0.19.1 |
