Image preprocessing is required to enhance the quality of input data and ensure consistency for machine learning models. Preprocessing steps like resizing, normalization, and noise reduction improve the model’s ability to learn meaningful patterns. For instance, resizing standardizes image dimensions, while normalization scales pixel values to a uniform range, preventing numerical instability during training. Removing noise and applying filters help focus on relevant features, improving accuracy. Preprocessing ensures that the input data is clean, uniform, and optimized for reliable and efficient model performance.
Why is image preprocessing required?

- Optimizing Your RAG Applications: Strategies and Methods
- Advanced Techniques in Vector Database Management
- Natural Language Processing (NLP) Basics
- How to Pick the Right Vector Database for Your Use Case
- Embedding 101
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How do robots perform tasks like grasping and manipulation of objects?
Robots perform tasks like grasping and manipulation of objects through a combination of hardware and software components
How do hybrid models enhance speech recognition systems?
Hybrid models enhance speech recognition systems by combining two or more distinct approaches to improve accuracy and pe
How does observability handle partitioning in distributed databases?
Observability in distributed databases plays a crucial role in handling partitioning by providing insights into data dis