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I'm new to YOLO and trying to make car counting application using YOLO. The cars is from video file. Is there any reference? Thank you
There are multiple places from which I suggest you try to learn:
1) The CNN course from coursera https://www.coursera.org/learn/convolutional-neural-networks
This course has a good explanation on yolo(There assignment is on car detection as well which can easily be extended to car counting) and the rest of the course is quite nice as well
2)https://towardsdatascience.com/yolo-you-only-look-once-real-time-object-detection-explained-492dc9230006
The article focus on a few implementation details and talks about the papers yolo and yolov2 , and helped me clear a out a few issues i had when i was trying to implement yolo
3)The original paper (although this may be too advanced ): https://arxiv.org/pdf/1506.02640v5.pdf
4)A keras implmentation : https://github.com/experiencor/keras-yolo2
A simple git clone if you wish to simply have the code , although i do not recommend this as it has very little actual learning and is simply a download and use option
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Hey I want to use word2vec algorithm without implementing it (I saw a lot of places that teaches how to implement one).
does anyone can tell me what is the best lib to use?
I saw there is Genesim, and Deeplearning4j. also TensorFlow but I can't find a place where they have the function that I need (only how to implement with this lib).
can someone give some comparison about efficiency? how easy to use? the word2vec algorithm for each lib?
any helpful tip or resources would be great.
SpaCy comes with pretrained word embeddings that you can use - it's very easy to use, you can find examples on how to download the embedding and use it here.
The implementation in the Python gensim library offers a Word2Vec model class that is flexible (with options not available elsewhere) & as fast as the original word2vec.c code released by the Google researchers who created the algorithm.
You can see its docs at:
https://radimrehurek.com/gensim/models/word2vec.html
There's an intro tutorial that can be run as in a 'Jupyter' notebook:
https://radimrehurek.com/gensim/auto_examples/tutorials/run_word2vec.html
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I am trying to do a paper about the Machine learning been applied in NLP. Can you guys please suggest me applications that have already used the Machine learning with the NLP?
The list is broad since machine learning is becoming more and more mainstream.
Regarding text, images and video, a good list of APIs would be:
AT&T Speech, IBM Watson, Google Prediction, Wit.ai, AlchemyAPI, Diffbot and I guess Project Oxford as well.
Hope it helps.
If you want something generic you can use this tutorial: http://www.cs.columbia.edu/~mcollins/papers/tutorial_colt.pdf
It is probably not the more recent information but you could find it useful if you start to learn ML methods for NLP.
As it is mentionned in this tutorial, ML methods are generally linked to the NLP task (Information Extraction, Machine Translation, etc.).
IBM Watson project is an example of platform that uses NLP and ML.
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Does anyone know of or have a good tool for labeling image data to be used in training a DNN?
Specifically labeling 2 points in an image, like upperLeftCorner and lowerRightCorner, which then calculates a bouding box around the specified object. That's just an example but I would like to be able to follow the MSCoco data format.
Thanks!
You might try LabelMe, http://labelme.csail.mit.edu/Release3.0/
It's usually for outlines for segmentation, but I'm pretty sure it works fine for bounding boxes too.
I had a similar issue finding a tool that did bouding boxes for labeling image data, so I started this new project called LabelD (https://github.com/sweppner/labeld) that uses NodeJS and focuses on bouding boxes for annotation. It's still very much in alpha, but it's pretty easy to use and functional for labeling images!
Let me know if you have any questions!
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I was curious if anyone knew of something like this flowchart but for Computer Vision tasks? Specifically for OpenCV would be most ideal.
Or any references with best practices, and common patterns for Computer Vision problems?
That's a monumental task. The best I could find is from this article and it's a little bit old:
Maybe it's a good time to commit to FlexCV on Kickstarter.com, a GUI for OpenCV that allows you to create complex algorithms in a matter of minutes by connecting graphical elements together. It's an alternative for Adaptive Vision, but purely based on OpenCV features.
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I have found OpenCV code that uses CvGaussBGStatModelParams structure, cvCreateGaussianBGModel, and other related functions. However, I haven't been able to find any explanations for how they work and how they are to be used and what they mean.
Any help would be appreciated.
These functions are undocumented (at least as far as the manual goes). However, if you look around in the source, you will find them in src/cvaux/cvbgfg_gaussmix.cpp. In there:
This is based on the "An Improved
Adaptive Background Mixture Model for
Real-time Tracking with Shadow
Detection" by P. KaewTraKulPong and R.
Bowden
http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
The windowing method is used, but not
the shadow detection. I make some of
my own modifications which make more
sense. There are some errors in some
of their equations.
That link is probably a good start for you.