vit-base-patch16-224-Futurama_Image_multilabel_clf
This model is a fine-tuned version of google/vit-base-patch16-224.
It achieves the following results on the evaluation set:
- Loss: 0.0592
- F1: 0.9818
- Roc Auc: 0.9842
- Accuracy: 0.9672
Model description
This is a multilabel classification model of screenshot images from the show Futurama.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multilabel%20Classification/Futurama%20Screenshots/Futurama%20-%20ML%20Image%20CLF.ipynb
Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/gonzalorecioc/futurama-frames-with-characteronscreen-data
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.2456 | 1.0 | 916 | 0.0723 | 0.9711 | 0.9746 | 0.9481 |
| 0.0269 | 2.0 | 1832 | 0.0545 | 0.9799 | 0.9818 | 0.9640 |
| 0.0086 | 3.0 | 2748 | 0.0580 | 0.9794 | 0.9814 | 0.9623 |
| 0.0044 | 4.0 | 3664 | 0.0612 | 0.9814 | 0.9832 | 0.9651 |
| 0.0027 | 5.0 | 4580 | 0.0592 | 0.9818 | 0.9842 | 0.9672 |
| 0.0017 | 6.0 | 5496 | 0.0634 | 0.9800 | 0.9832 | 0.9645 |
| 0.0012 | 7.0 | 6412 | 0.0657 | 0.9817 | 0.9840 | 0.9667 |
| 0.0005 | 8.0 | 7328 | 0.0668 | 0.9812 | 0.9836 | 0.9667 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.12.1
