Evolutionary YOLO
- Python 3.6.7
- Jupyter Notebook
Clone this repository and AlexeyAB/darknet, respectively.
$ git clone https://github.com/tsukar/evolo.git
$ git clone https://github.com/AlexeyAB/darknet.git
$ cd darknet/
Edit the Makefile
so that you can use your GPU.
@@ -1,7 +1,7 @@
-GPU=0
-CUDNN=0
+GPU=1
+CUDNN=1
CUDNN_HALF=0
-OPENCV=0
+OPENCV=1
AVX=0
OPENMP=0
LIBSO=0
@@ -30,7 +30,7 @@ OS := $(shell uname)
# ARCH= -gencode arch=compute_72,code=[sm_72,compute_72]
# GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4
-# ARCH= -gencode arch=compute_61,code=sm_61 -gencode arch=compute_61,code=compute_61
+ARCH= -gencode arch=compute_61,code=sm_61 -gencode arch=compute_61,code=compute_61
# GP100/Tesla P100 <96> DGX-1
# ARCH= -gencode arch=compute_60,code=sm_60
Just make
and copy the compiled binary into the root directory of this repository.
$ make
$ cp darknet ../evolo/
$ cd ../evolo/
Download the pre-trained weights from YOLOv2 official site.
$ wget https://pjreddie.com/media/files/darknet19_448.conv.23
Download train.zip and test.zip (currently not available - please contact me at tsukada [at] iba.t.u-tokyo.ac.jp
if you need them). Put the decompressed *.jpg
files in data/x-ray/train/
, data/x-ray/test/
, respectively.
Run Jupyter Notebook and open evolution.ipynb
-> Run All
to start evolution.
$ jupyter notebook
After evolution, open evaluation.ipynb
-> Run All
for evaluation.