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?
- How to Pick the Right Vector Database for Your Use Case
- Master Video AI
- Retrieval Augmented Generation (RAG) 101
- Information Retrieval 101
- Mastering Audio AI
- 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 are predictive AI agents?
Predictive AI agents are software systems designed to analyze data and make informed predictions about future events or
How does caching affect benchmarking results?
Caching can significantly affect benchmarking results by altering the way data is retrieved and processed during tests.
How do multi-agent systems handle resource allocation?
Multi-agent systems (MAS) manage resource allocation by enabling multiple agents to interact, negotiate, and cooperate t