Fine-Tune Vision AI Models That Beat GPT-4 | Fine Tuning Gemma 3 4B with Datawizz
Learn how to train specialized vision models that outperform GPT-4.1 while being faster and cheaper! In this comprehensive tutorial, I'll show you how to fine-tune the Gemma 3 4B model on the Datawizz platform to create a food recognition AI that extracts dish names, ingredients, nutritional info, and portion sizes from images.
We'll use the MMFood100K dataset and create custom evaluators to benchmark our model against GPT-4.1, proving that smaller, specialized models can deliver better results for domain-specific tasks.
🚀 What You'll Learn:
- Fine-tuning vision models on custom datasets
- …
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Chapters (12)
Introduction & Demo Overview
0:45
Dataset Overview (MMFood100K from Hugging Face)
1:33
Creating the Prompt Template in Datawizz
4:10
Importing & Preparing the Dataset
7:10
Fine Tuning the Model
9:09
Training Results & Loss Curves
10:20
Manually Testing the Model
12:44
Creating Custom Evaluators
19:36
Running Full Evaluation Suite
21:10
Benchmark Results & Analysis
24:00
Creating Production Endpoints
25:04
Summary & Conclusion
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