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You learn:

- Complete process about different deep learning object detectors creation and training (mobile and desktop)

- Install, compile and use DLIB Desktop(CPU, GPU)

- Install and use CUDA + CUDNN for GPU training and inference aceleration (Nvidia videocard needed)

- Install and use Visual studio Community for Compile and develop AI programs(DLIB)

- Use imglab for data anotation (image labeling) in DLIB format

- Use imglab for data splitting (train and test)

- Install and use imglabaug (python) fork for DLIB object detection(data augmentation) 

- All proccess for train multiclass object detectors with DLIB (image labeling, Parameter tunning, set the xmls path on debug arguments, data augmentation, etc)

- Tuning parameters, arguments and neural network arquitecture elements for speed up the training and inference time (DLIB mobile)

- Define and use especialized custom neural network arquitecture (exclusive own solution) to solve the biggest problem of "tiny object detection" for detect and recognize very small things on images.

- Deserialize and use neural networks trained in object detection program (DLIB .Dat)

- Install and use VirtualBOX to mount Linux ubuntu 18.04 to compile OpenBlas and Android Dlib IA Projects

- Generate standalone android toolchain in linux for compile OpenBlas and generate the .so and .a files to use for Android object detection app

- Compile, install and use OpenBLAS in linux for mobile inference aceleration (acelereration for ARMv7-32 bits, ARMv8-64bits and especial Cortex arquitecture)

- Install and use NDK (Android C, C++ compiler) in Android studio for mobile DLIB object detection apps using big desktop models (not quantized or low precision lite versions for mobile)

- Use DLIB and link with OpenBLAS to acelerate and run your own object detection models in Android studio(Camera multiclass object detection own app)

- Install and use intel MKL + ThreadBB for CPU training/inference aceleration

- All proccess for train multiclass object detectors with TensorFlow for mobile apps.

- Use tensorflow lite trained models in real time on android.

- Install and use Jupyter noteboks for interactive training, eval and testing of Tensorflow models (Python)

- Use google Colab for interactive training, eval and testing of Tensorflow models online if you not have GPU (Python)

- Install and use anaconda for create tensorflow GPU enviroments(Drivers, CUDA, CUPTI, etc) automatically.

- Run and test tensorflow lite trained models in desktop or mobile.

- Convert formats with exclusive own program (DLIB to Pascal VOC and Pascal VOC to DLIB) for interchange plataforms and make diferent projects easy (DLIB and Tensorflow).

Requirements for course:

- Only basic programing experience in any languaje.

- (Optional) Nvidia Graphic Card with at least 4gb of Vram for training proccess with CUDA (GTX, RTX, Quadro, etc)

- Want to learn a lot!

You get: 

- Access to our own moodle education platform for ordered and easy lessons learning (access for life)

- Very practical lessons for understand and produce your own object detectors for you or your enterprise

- All special architectures for train different type objects (for size, amount of data, form, etc)

- All source code of all programs used in the course lessons

- Step by step lessons and tutorials for easy learning and understanding of topics.

- Very exclusive information and programs not available on the net.

- Knowledge, secrets and experience for deep learning object detectors reflected in the lessons.

- Direct contact with teacher (AI MSc. Sergio Andrés Gutiérrez Rojas)