Yolo training

https://groups.google.com/forum/#!topic/darknet/WvBFz4zSSH4 fine tuning

train
https://eavise.gitlab.io/lightnet/notes/02-examples.html

http://www.renom.jp/notebooks/image_processing/yolo/notebook.html

https://blogs.sap.com/2018/07/25/object-detection-with-yolo-for-intelligent-enterprise/

https://timebutt.github.io/static/how-to-train-yolov2-to-detect-custom-objects/

http://guanghan.info/blog/en/my-works/train-yolo/

https://medium.com/@manivannan_data/how-to-train-yolov3-to-detect-custom-objects-ccbcafeb13d2

https://medium.com/@jonathan_hui/real-time-object-detection-with-yolo-yolov2-28b1b93e2088

https://medium.com/@manivannan_data/how-to-train-multiple-objects-in-yolov2-using-your-own-dataset-2b4fee898f17

https://medium.com/@ribomo42/how-to-train-yolo-v2-with-your-own-data-object-and-labels-on-darknet-2b90dbfecb02

http://ashishkhan.com/blog/not-hotdog-app-with-darknet-yolo-face-detection

https://jumabek.wordpress.com/2017/03/04/how-to-train-yolov2-on-costum-dataset/

http://smart-city-sjsu.net/AICityChallenge/papers/NVIDIA_AI_City_Challenge_2017_paper_19.pdf

https://www.youtube.com/watch?v=MISwqExOjEI

33
https://mc.ai/yolo3-a-huge-improvement/

object tracking
http://www.cs.toronto.edu/~davidj/projects/towards_real_time_detection_tracking.pdf  Online multi-player  detection and  tracking in  broadcast basketball  videos are significant challenging tasks. In this environments, the target distributions are highly non-linear, and the varying number of objects creates complex interactions with overlap and ambiguities. In this paper, we present a real-time multi-person detection and tracking framework that is able to perform detection and tracking of basketball players on sequences of videos. Our framework is based on YOLOv2, a state-of-the-art real-time object detection system, and SORT, an object tracking framework based on data association and state estimation techniques. For training and testing, we use a given subset of the NCAA Basketball Dataset. As part of the bonus, we trained a two-layer LSTM to do action recognition

hacker news
https://news.ycombinator.com/item?id=15956426

https://www.slideshare.net/TaegyunJeon1/pr12-you-only-look-once-yolo-unified-realtime-object-detection slides

ai.stackexchange
https://ai.stackexchange.com/questions/2854/ssd-or-yolo-on-arm

forums
https://ai6forums.nurture.ai/t/questions-on-yolo/251

journals
https://brage.bibsys.no/xmlui/handle/11250/2418432 Recent advancements in machine learning, and in particular deep neural networks, have yielded excellent object detection models. However, these techniques require vast datasets of labeled training images, which are prohibitively labor intensive to produce.

This thesis explores an alternative approach to obtaining labeled training data, namely using 3D models of objects and modern game engines to generate automatically labeled synthetic training data. A simple approach for generation similar to the one used by Peng et al. (2014) is presented requiring minimal user input, making dataset generation virtually free.

https://nurture.ai/top-papers do search with  yolo

gpu
http://gpupowered.org/node/53/ and https://github.com/prabindh/darknet c++ port, euclid labeler (move this to notable forks)

freelancer jobs
https://www.freelancer.co.za/projects/machine-learning/face-recognition-based-yolo-darknet/ Looking for a freelancer with experience in YOLO for Face recognition. your job to create a DLL that will accept image as input, detect the faces in the image and compare between faces its already have in database. if face found in database return JSON of who's face it is. The DLL must also able to learn new faces. each time new face is introduced it should store it and group by same person face.

links
Yolo bounding box