So if you use C++ to do computer vision work then dlib's deep learning framework is for you. It makes heavy use of C++11 features, allowing it to expose a very clean and lightweight API. For example, the venerable LeNet can be defined in pure C++ with a using statement:
using LeNet = loss_multiclass_log< fc<10, relu<fc<84, relu<fc<120, max_pool<2,2,2,2,relu<con<16,5,5,1,1, max_pool<2,2,2,2,relu<con<6,5,5,1,1, input<matrix<unsigned char>>>>>>>>>>>>>>;
Then, using it to train and test a neural network looks like this:
LeNet net; dnn_trainer<LeNet> trainer(net); trainer.set_learning_rate(0.01); trainer.set_min_learning_rate(0.00001); trainer.set_mini_batch_size(128); trainer.train(training_images, training_labels); // Ask the net to predict labels for all the testing images auto predicted_labels = net(testing_images);
Dlib will even automatically switch to lower learning rates when the training error stops improving, so you won't have to fiddle with learning rate schedules. The API will certainly let you do so if you want that control. But I've been able to train a number of state-of-the-art ImageNet models without any manual fiddling of learning rates, which I find to be very convenient.
Depending on how you compile dlib, it will use either the CPU or cuDNN v5. It also supports using multiple GPUs during training and has a "fast mode" and a "low VRAM" mode. Compared to Caffe, dlib's fast mode is about 1.6x times faster than Caffe but uses about 1.5x as much VRAM, while the low VRAM mode is about 0.85x the speed of Caffe but uses half the VRAM as Caffe. So dlib's new deep learning API is fast but can also let you run larger models in the same amount of VRAM if you are VRAM constrained.
It's also fully documented. The basics are covered in this tutorial and then more advanced concepts are covered in a follow on tutorial. These tutorials show how to define LeNet and ResNet architectures in dlib and another tutorial shows how to define Inception networks. And even more importantly, every function and class in the API is documented in the reference material. Moreover, if you want to define your own computational layers, loss layers, input layers, or solvers, you can because the interfaces you have to implement are fully documented.
I've also included a pretrained ResNet34A model and this example shows how to use it to classify images. This pretrained model has a top5 error of 7.572% on the 2012 imagenet validation dataset, which is slightly better than the results reported in the original paper Deep Residual Learning for Image Recognition by He, Zhang, Ren, and Sun. Training this model took about two weeks while running on a single Titan X GPU.
To use the new deep learning tools, all you need to install is cuDNN v5. Then you can compile the dlib example programs using the normal CMake commands. There are no other dependencies. In fact, if you don't install cuDNN CMake will automatically configure dlib to use only the CPU and the examples will still run (but much slower). You will however need a C++11 compiler, which precludes current versions of visual studio since they shamefully still lack full C++11 support. But any mildly recent version of GCC will work. Also, you can use visual studio with the non-DNN parts of dlib as they don't require C++11 support.
Finally, development of this new deep learning toolkit was sponsored by Systems & Technology Research, as part of the IARPA JANUS project. Without their support and feedback it wouldn't be nearly as polished and flexible. Jeffrey Byrne in particular was instrumental in finding bugs and usability problems in early versions of the API.