A generalised training framework consisting of all kinds of models like Yolo, RCNN etc. with default model configurations.
This repo acts like a training guide for yolo object detection model got custom data.
To test that CUDA works, go to the CUDA demo suite directory:
cd /usr/local/cuda/extras/demo_suite/
./deviceQuery
Download the yolov3 weights in Darknet dir:
wget https://pjreddie.com/media/files/yolov3.weights
Make Sure in Makefile 'gpu == 1'.
From darknet dir run make
For Testing Darknet: ./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
Copy "yolov3.cfg" file from cfg to custom_data/cfg dir, and rename to yolov3-custom.cfg
The maximum number of iterations for which our network should be trained is set with the param max_batches=4000. Also update steps=3200,3600 which is 80%, 90% of max_batches, you can set value based on your training requirements.
classes param in the yolo layers to based on number of classes you are workning with like for one or 2 class at line numbers: 610, 696, 783.
Similarly we will need to update the filters param based on the classes count filters=(classes + 5) * 3.
For a single class we should set filters=18 at line numbers: 603, 689, 776.
classes=1
train=custom_data/train.txt //Path to text file of images path for training.
valid=custom_data/test.txt // Path to text file of images path for testing.
names=custom_data/custom.names //Path to the class names
backup=backup/ //path to save weights
Test.txt need to store the path of each image used for testing
Train.txt need to store the path of each image used for training
./darknet detector detector train custom_data/detector.data custom_data/cfg/yolov3-custom.cfg yolov3.weights./darknet detector test custom_data/detector.data custom_data/cfg/yolov3-custom.cfg backup/yolov3_final.weights -ext_output -out eval.json < eval.txt
./darknet detector map data/obj.data custom_data/cfg/yolov3-custom.cfg backup/yolov3_final.weights
./darknet detector recall data/obj.data custom_data/cfg/yolov3-custom.cfg backup/yolov3_final.weights#note eval.json will store all the output bounding box for each input image path stored in the eval.txt, //eval.txt will be prepared exactly like test.txt/train.txt