What Exactly the Caffe2 is ?
Caffe2 is a deep learning Framework , it is a Convolutional Architecture for Fast Feature Embedding.
Convolutional neural network it is a kind of operations like linear Operations , it is a class of deep neural networks ,It is most commonly used to analyzing visual imagery which is very important while evaluating an image and understanding the meaning of that evaluated data .
Now , we will understand the difference between traditional machine learning and deep learning . Last couple of years, Deep Learning has become a big trend in Machine Learning . It has been successfully applied to solve previously unsolvable problems in Vision, Speech Recognition and Natural Language Processing (NLP) .
Deep learning has had exciting progress in the last few years especially in supervised learning tasks in computer vision, language, speech, etc.wikipedia.org
The Caffe project was created by Yangqing Jia during his Ph.D. at University of California – Berkeley . It is written in C++ and provides bindings for Python and Matlab. It supports many different types of deep learning architectures such as CNN ( Convolutional Neural Network ) , LSTM (Long Short Term Memory) and FC (Fully Connected). It supports GPU and so why , It ideally suited for production environments involving deep neural networks .
Machine Learning v/s Deep Learning
In any machine learning algorithm, be it a traditional one or a deep learning one, the selection of features in the dataset plays an extremely important role in getting the desired prediction accuracy.
In deep learning algorithms, feature engineering is done automatically. Generally, feature engineering is time-consuming and requires a good expertise in domain. To implement the automatic feature extraction, the deep learning algorithms typically ask for huge amount of data.
Training a CNN
This is how we trained our model
What’s New in Caffe2
In Caffe2, you would find many ready-to-use pre-trained models and also leverage the community contributions of new models and algorithms quite frequently.
The improvements made in Caffe2 over Caffe may be summarized as follows −
- Mobile deployment
- New hardware support
- Support for large-scale distributed training
- Quantized computation
- Stress tested on Facebook
Pretrained Model Demo
In the above image of a dog is classified and labelled with its prediction accuracy. It also says that it took just 0.068 seconds to classify the image.
Open Command Promt and run the following command
conda install -c pytorch pytorch-nightly-cpu
and after the above command run this
conda install -c anaconda zeromq
Caffe2, which is a deep learning framework allows you to experiment with several kinds of neural networks for predicting your data. Caffe2 site provides many pre-trained models. You learned to use one of the pre-trained models for classifying objects in a given image. You also learned to define a neural network architecture of your choice. Such custom networks can be trained using many predefined operators in Caffe. A trained model is stored in a file which can be taken into a production environment.
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