Last Updated on February 19, 2021 by Editorial Team
Author(s): Buse Yaren Tekin
Deep Learning
Let’s discuss it together…
☕️ Hello from another day with an intense coffee scent. In this article, I would like to introduce you to a beautiful deep learning framework structure. The human brain can be considered the world’s most complex machine [1]. We continue our way with artificial neural networks.
I met with a new framework while continuing my research in the past few days. Let me present this sweet framework structure to you. Developed by Berkeley AI Research (BAIR) and community contributors, Caffe can work very quickly and efficiently in image classification.
Caffe; it is a deep learning framework prepared by considering expression, speed and modularity [3].
So Why Caffe?
- It has a modern structure in both code and model.
- When it comes to speed, it can handle over 60 million images in a day with the only NVIDIA K40 GPU * processor.
- It currently powers academic research projects, enterprise prototypes and even industrial applications in the large-scale field of vision.
- Open framework offers models and worked examples for deep learning.
📣 We can say that it is impossible not to applaud while offering such great opportunities. Now let’s examine the necessary approaches in terms of coding and models. I am an active Python user. And frankly, I am very glad that it can work with Python. Let’s examine the items below.
- Pure C ++ / CUDA library for deep learning
- Command-line, Python, MATLAB interfaces
- Fast, well-tested code
- Tools, reference models, demos and recipes
- Seamless transition between CPU and GPU
📣 To access Caffe’s original codes, simply click on the Github link.
📣 You can continue on the link to access all the documentation.
For example, I want to collaborate with the Mask RCNN network that I use during image segmentation. Because according to the information I have read, Caffe is believed to be the fastest ConvNet application available.
📣 I share with you a Github content created in order to be compatible with Mask RCNN.
📣 If you are doing instant recognition during the image classification stage, you can access the Jupyter notebook page via the link.
📌 If there is a special data set available, you can examine the usage of Caffe with the special data set over the link.
🔮 For more content, you can follow me on my GitHub and Linkedin accounts!
REFERENCES
- Prof. Dr. Cetin ELMAS, Artificial Intelligence Applications, 2nd Edition, October 2010, Publishing Seckin.
- Taken from the https://medium.com/@alexrachnog/using-caffe-with-your-own-dataset-b0ade5d71233.
- Berkeley Vision, Caffe, https://caffe.berkeleyvision.org.
- DIY Deep Learning for Vision: a Hands-On Tutorial with Caffe, Document.
What Kind of Framework Is Caffe? was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
Published via Towards AI