Multimodal Data Classification with Gemini 2.0
This challenge focuses on leveraging Google's Gemini 2.0 for multimodal data classification. Participants will build a system that accurately classifies data incorporating images, text, and potentially audio. The challenge emphasizes efficient data preprocessing, model selection, and evaluation metrics relevant to multimodal understanding. Solutions should demonstrate a clear understanding of data fusion techniques and the challenges involved in working with multimodal data.
What you are building
Build a multimodal data classification system using Gemini 2.0, showcasing efficient data handling and model selection.
How work is evaluated
Evaluation based on classification accuracy, precision, recall, and F1-score across different modalities.
Datasets
Public datasets, plus the private datasets you have access to.
Learning goals
Analyze and interpret the model's performance and identify areas for improvement.
Document the entire process, including data preprocessing, model selection, and results.
Select and fine-tune a suitable model for multimodal classification.
Implement appropriate evaluation metrics for multimodal data.
Preprocess and integrate diverse data modalities (image, text, potentially audio).
Resources
Sample data available for 1 evaluation tasks
Challenge setup
Evaluation based on classification accuracy, precision, recall, and F1-score across different modalities.
Challenge input
A dictionary containing image, text, and optional audio data.
Agent execution
No agent tool is listed for this challenge.
Evaluated output
A predicted class label.
- No evaluation checks are configured.
- Accuracy target: 70
- Python execution harness
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- No evaluation checks are configured.
Metrics
- Accuracy · Target 70
- F1-Score · Target 65
Workflow tasks
- Task 1
Input: A dictionary containing image, text, and optional audio data.
Output: A predicted class label.
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.