license: mit language:
- en pipeline_tag: image-classification tags:
- images datasets:
- aedupuga/cards-image-dataset metrics:
- accuracy
- f1 library_name: autogluon
Training Details: -The model was trained using AutoGluon's MultiModalPredictor with the following configuration:
-Problem Type: Classification -Evaluation Metric: Accuracy -Presets: medium_quality -Hyperparameters: -model.names: ["timm_image"] -model.timm_image.checkpoint_name: "resnet18" -The training data used was the 'augmented' split of the dataset, with a 80/20 train/test split for tuning.
Evaluation: -The model was evaluated on the 'original' split of the dataset.
-Accuracy: 1.0000 -Weighted F1: 1.0000 -Note: These results are based on the evaluation performed in the provided Colab notebook.