Switch to Gemini 1.5 Flash for High-Volume Multimodal Task Classification
Use Gemini 1.5 Flash to categorize images with minimal training data.
Use In-Context Learning (ICL) to perform image classification by providing a few labeled examples in a single prompt to Gemini 1.5 Flash.
The Scenario
You have a large folder of product photos or user-generated content that needs to be categorized into specific business-defined buckets like 'Outdoor', 'Indoor', or 'Close-up'.
Before & after
Manually reviewing hundreds of images to categorize them for a database or website takes hours of clicking and tagging (e.g., 60–90 minutes for 100 images).
By selecting Gemini 1.5 Flash in Google AI Studio, you can automate labeling against your custom classes. This takes about 2–3 minutes to set up the prompt and run.
The Prompt
I am providing [NUMBER] example images with their correct labels: [EXAMPLES]. Now, please classify the following new image based on those examples. Output only the category name: [LINK_TO_NEW_IMAGE_OR_UPLOAD]
Gemini 1.5 Flash is significantly more cost-effective for multimodal tasks compared to Pro or GPT-4o. It excels at 'In-Context Learning' where you provide a few examples in the prompt to help it understand your specific classification criteria.
Source
Release notes | Gemini API | Google AI for Developers"Multimodal Large Language Models (MLLMs) with ICL can be used to perform more traditional ML tasks such as image classification."