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?
- Exploring Vector Database Use Cases
- How to Pick the Right Vector Database for Your Use Case
- Embedding 101
- Vector Database 101: Everything You Need to Know
- Natural Language Processing (NLP) Basics
- 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 stochastic optimization in swarm intelligence?
Stochastic optimization in swarm intelligence refers to a method of solving optimization problems using a population-bas
How does Elasticsearch work as a document store?
Elasticsearch is a distributed search and analytics engine that serves as a document store by allowing users to store, s
What is the difference between graph and vector embeddings?
Graph embeddings and vector embeddings are two different techniques used to represent data, but they serve different pur