Build and deploy deep learning models with speed and flexibility
Caffe is a deep learning framework that lets you define models through configuration, so you don't need to hard-code everything. It's incredibly fast, processing millions of images daily, which is perfect for research and real-world applications. You can easily switch between CPU and GPU, making it versatile for different hardware setups.
Define models by configuration without hard-coding
Fosters active development and community contributions
Processes over 60M images per day on a single GPU
Switch between hardware with a single flag
Join a large community of developers and users
Caffe is a deep learning framework developed by Berkeley AI Research, focusing on expression, speed, and modularity for building and deploying models.
Caffe can process over 60 million images per day with a single NVIDIA K40 GPU, making it one of the fastest convnet implementations available.
Installation instructions are provided for Ubuntu, Red Hat, and OS X on the Caffe website.
Yes, Caffe is released under the BSD 2-Clause license and is open-source, encouraging community contributions.
This tool is best for:
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Use Caffe on your favorite device - available across multiple platforms for flexibility.
Recent data shows that Caffe has 1.4K monthly visits (-72.2% decrease from the previous month), 55.0% bounce rate, and average 1.96 pages per visit.
Traffic is primarily driven by 6 different sources, with users from 3 countries worldwide, led by United States contributing 69% of total traffic.
Monthly Visits
1.4K
Pages per Visit
1.96
Bounce Rate
55.0%
Average Time on Site
3m 40s
| Source | Traffic Share |
|---|---|
| Direct | 47% |
| Search | 37% |
| Social | 8% |
| Referrals | 7% |
| Paid Referrals | 1% |
| Country Name | Traffic Share |
|---|---|
 | 69% |
 | 19% |
 | 11% |
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