Objects detection and localisation with Ultra96

Ultra96-yolo

Yolo object detection for Avnet/Xilinx Ultra96 board (Arm53 cores+fpga), based on a school assignment by deeplearning.ai.
This is a basic Yolo implementation in C, without hardware acceleration - Usage Instruction

Performance: execution on mono-core takes about 2 minutes for detection in a 780x1024 image.

1) Clone the repository onto Ultra96 board, in home directory "/home/root". After cloning:
     /home/root/Ultra96-yolo

2) To compile all in Ultra96-yolo:
    cd /home/root/Ultra96-yolo/itf
    chmod a+x compile.sh
    ./compile.sh
    
        Display in console  is like:
        running build_ext
        ...
        aarch64-xilinx-linux-gcc ... -o /home/root/Ultra96-yolo/itf/convNetC.so	

3) to run yolo with input taken from ./images, output placed into ./out:    
    cd /home/root/Ultra96-yolo/
    python test_C.py
	
        Display in the console while running "test_C.py" is like:
        root@Ultra96:~/Ultra96-yolo# python test_C.py
         ---------------- input image: test.jpg in ./images, output image: test.jpg in ./out -------------
        Have detected 6 objects. consomed time: 2018-11-15 00:24:06.529748 -> 2018-11-15 00:25:48.236639
        
         ---------------- input image: 0019.jpg in ./images, output image: 0019.jpg in ./out -------------
        Have detected 2 objects. consomed time: 2018-11-15 00:25:48.346454 -> 2018-11-15 00:27:29.844256

4) Description of the files
	./README.md
	./Drive.ai+Dataset+Sample+LICENSE.html
	./LICENSE.txt
	
	./test_C.py: run this C implementation of Yolo on input images ./images/* to produce the out/*. 
	             In the output image, the recognized objets are surrounded by boxes.
	
	./itf: interface for Python user applications such as 'test_C.py'.
	./itf/compile.sh: compile Python interface module 'convNetC'.
	./itf/convNetC_itf.c: source code of Python interface. Calling conv_net.c.
	./itf/setup.py: 'makefile' to compile this C implementation, used by "compile".
	./itf/__init__.py: empty file for python to take "itf" as a importable module.	
		
	./lib:  includes C implementation of convolutional net for inference.
	./lib/conv_net.c: C subroutines used when making inference. Calling convNetC_fct.c.
	./lib/conv_net.h: prototype for #include.
	./lib/utils.py: image resizing, image reading and image saving in Python.
	./lib/__init__.py: empty file for python to take "lib" as a importable module.	
	
	./model/convNetC_fct.c: C API to the model. Called by conv_net.c.
	./model/convNetC_var.c: variables and static memory used for the model.
	./model/convNetC_w.BE: weights of the model in big-endian.
	./model/convNetC_w.LE: weights of the model in little-endian.