Update README.md

Model Details
This model was trained on the aedupuga/cards-image-dataset for the task of image classification.

The model architecture used is timm_image with the resnet18 checkpoint, as determined by AutoGluon's AutoML process.

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 Results
The model was evaluated on the original split of the dataset with an accuracy and weighted F1 of 1.00000.

Usage
You can load and use this model with AutoGluon.

README.md

@@ -1,3 +1,10 @@
+---
+tags:
+- autogluon
+- multimodal
+- image-classification
+- resnet18
+---
 license: mit
 language:
   - en