How to train YOLO-Tensor flow own dataset

The best data for you to practice is PASCAL VOC dataset. There are 2 folders you need to prepare for the training. 1 folder with images and 1 folder with xml files(annotation folder), 1 image will need 1 xml file (have the same name) content all the basic informations (object name, object position, ...). after that you only need to choose 1 predefine .cfg file in cfg folder and run the command follow:

flow --model cfg/yolo-new.cfg --train --dataset "path/to/images/folder" --annotation "path/to/annotation/folder"

Read more the options supported by darkflow to optimize more the training process.

After spending too much time on how to train custom data set for object detection

Prerequisite : 1:training environment : a system with at least 4gb GPU or you can use AWS / GCP pre-configured cloud machine with cuda 9 installation 2: ubuntu 16.04 os 3: images of the object you want to detect. images size should not be too much large it will create out of memory issue in dataset training 4: labelling tool many are available like LabelImg/ BBox-Label-Tool i used is also good one I tried python project dataset-generator also but result of labelling using dataset generator was not efficient in real time scenarios

My suggestion for training environment is to use AWS machine rather than spend time in local installation of cuda and cudnn even though you are able to install cuda locally but if you are not having GPU >= 4 gb you will not be able to train many times it will break due to out of memory issue

solutions to train data set :

1: train ssd_mobilenet_v2 data set using tensorflow object detection api this training output can be use on both android , ios platform

2: use darknet to train data set which required pascal VOC data format of labelling , for that labelIMG can do the job of labelling very good 3: retrain that data weights which comes as output from darknet with darkflow