Caffe is a widely used deep learning framework known for its flexibility and efficiency in handling complex neural network architectures. Below are key points about Caffe:
📘 Overview
- Developed by: Berkeley AI Research (BAIR)
- Language: C++ (with Python/ Matlab interfaces)
- Purpose: Image classification, segmentation, and other computer vision tasks
- Key Features:
- Modular design for easy customization
- Fast training speed due to GPU acceleration
- Extensive pre-trained models (e.g., AlexNet, VGG)
🌱 History
Caffe was introduced in 2013 and became popular for its simplicity and speed. It has since influenced many other frameworks like TensorFlow and PyTorch.
📚 Resources
- Caffe Tutorials for hands-on practice
- Caffe Model Zoo to explore pre-trained networks
📷 Visual Aids
For deeper insights, visit our Caffe Documentation Page. 🌐