CNN (Convolutional Neural Network) and R-CNN (Region-based CNN) are both used in computer vision, but they serve different purposes. CNNs are general-purpose models for tasks like image classification, while R-CNNs are designed specifically for object detection. CNNs process entire images, using convolutional layers to extract features and classify the image into predefined categories. For example, a CNN can identify whether an image contains a cat or a dog. R-CNNs extend CNNs by identifying regions of interest (ROIs) in an image and applying a CNN to each region for object detection. R-CNN is slower than CNN because it requires generating and processing multiple ROIs, but it excels in detecting and classifying multiple objects in an image.
What is the difference between CNN and R-CNN?

- Optimizing Your RAG Applications: Strategies and Methods
- Mastering Audio AI
- GenAI Ecosystem
- Natural Language Processing (NLP) Basics
- Accelerated Vector Search
- 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
What is the future of data governance?
The future of data governance is likely to be centered around increased automation, enhanced security measures, and a gr
What is an ER (Entity-Relationship) diagram?
An Entity-Relationship (ER) diagram is a visual representation of the entities in a system and the relationships between
How does database observability work in cloud environments?
Database observability in cloud environments refers to the ability to monitor, analyze, and understand the performance a