Fine-Tuning
What Is Fine-Tuning?
Fine-tuning is the process of taking a machine learning model that has already been trained on a large, general dataset and adapting it to perform better on a specific, targeted task. This approach allows companies to achieve high performance even with limited data by building on existing knowledge within the model.
Analyze Your Use Case
NYRIS uses fine-tuning to optimize visual search technology and image recognition systems for unique industry needs, ensuring rapid and precise product identification.
How Does Fine-Tuning Work?
- Start with a Pre-Trained Model: A model is first trained on a large, diverse dataset (such as ImageNet for images), learning to recognize broad patterns and features.
- Adaptation to Specific Tasks: The model is then fine-tuned by retraining it on a smaller, domain-specific dataset. This step helps the model specialize in recognizing features relevant to the new application, such as spare parts in manufacturing or products in retail.
- Deployment and Optimization: The fine-tuned model is integrated into business workflows, delivering fast and accurate results. NYRIS implements fine-tuning to ensure its visual search engine can process over 500 million products in under a second, tailored to each client's requirements.
Use Cases
- Manufacturing: Fine-tuning enables rapid identification of spare parts from images, reducing machine downtime by up to 90%. NYRIS partners with industry leaders like DMG Mori and Trumpf to streamline maintenance and after-sales service.
- E-commerce: By fine-tuning models for specific product categories, NYRIS empowers retailers like IKEA to offer shoppable content and visual product discovery, enhancing the online shopping experience.
- Retail Inventory Management: Fine-tuned models support automated shelf monitoring and inventory checks, helping companies like METRO improve stock accuracy and operational efficiency.
Benefits For Your Company
- Up to 85% Reduction in Manual Processes: Automate identification and classification tasks, freeing up valuable staff resources.
- Recognition Accuracy up to 99.7%: Achieve industry-leading precision in image recognition and visual search, minimizing errors and returns.
- Faster Time-to-Market: Deploy AI solutions quickly by leveraging existing models and fine-tuning them for your specific needs, reducing the need for extensive labeled data.
FAQs
How does fine-tuning improve NYRIS’s visual search technology?
Fine-tuning allows NYRIS to adapt its AI models to each client’s unique data, ensuring high accuracy and relevance in product identification and search.
Can fine-tuning be used with small or specialized datasets?
Yes, fine-tuning is especially effective when target datasets are limited, as it builds on robust features learned from large, general datasets.
What makes NYRIS’s fine-tuning approach unique?
NYRIS combines fine-tuning with synthetic data generation and deep learning, optimizing recognition for large-scale enterprise use and seamless integration with platforms like SAP.
About NYRIS
Founded in 2015 by Anna and Markus Lukasson-Herzig, NYRIS is a leading innovator in visual search technology and AI-driven solutions for manufacturing, e-commerce, and retail. With €10 million in funding from investors such as Trumpf Venture, EIC, and IKEA, NYRIS processes over 500 million products in less than a second. The company is recognized for its expertise in feature extraction, synthetic data generation from CAD models, and strategic partnerships with industry leaders like SAP, DMG Mori, and METRO. NYRIS’s pioneering use of fine-tuning ensures fast, accurate, and scalable image recognition for enterprise clients.