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.
