Also notable in G-Darknet are some tools useful for training (called darkboard), see https://github.com/generalized-iou/g-darknet/tree/master/dar...
A bit confusing as the drawn boxes don't match the text. Works with Chromium though.
Though I have a question: in order to calculate C you need a way to attribute proposal and ground truth. It's trivial in case when there's only one instance of each class in the image.
But how does it work, when you're working with a set of same-class object? For example detecting each car in traffic.
Alternatively... you can train in darknet and then run inference in another framework of choice.
Also shameless plug: I wrote an annotation tool which is designed to output darknet formatted labels: https://github.com/jveitchmichaelis/deeplabel
Thank you for the links! I'm going to check both out. I want to see if the PyTorch port works with the new deployment feature from 1.0.
Jokes aside, we need better temporal consistency, especially when we start arming AI. citizen -> citizen -> citizen -> armed insurgent
Of course, that matters far less when Skynet decides that every human is a hostile armed insurgent...
One of the first priorities of an operation is not knowing where your enemy is, but where you are.
And in general, due to its head, it is WAY more readable in PyTorch than in TensorFlow; to the point, I use it as an example in Keras vs PyTorch example https://deepsense.ai/keras-or-pytorch/ (was here at some point).
Is there any "continuous" models for that? Sounds like a simple bayesian post-processing would do a great deal (e.g. recording the probability of dogs mutating to teddy bears as very low).
You could still look only once, but have that look include multiple sequential frames. Or do something like an LSTM of frames.
Sounds to good to be true. Also reads like that. :) A gem from this paper:
But maybe a better question is: “What are we going todo with these detectors now that we have them?” A lot ofthe people doing this research are at Google and Facebook.I guess at least we know the technology is in good handsand definitely won’t be used to harvest your personal infor-mation and sell it to.... wait, you’re saying that’s exactlywhat it will be used for?? Oh.Well the other people heavily funding vision research arethe military and they’ve never done anything horrible likekilling lots of people with new technology oh wait..... 1
1 The author is funded by the Office of Naval Research and Google.
As a shameless plug, I wrote an intuitive guide to understanding SSD (Single Shot Detector), another popular object detection technique: https://towardsdatascience.com/understanding-ssd-multibox-re...
Xnor's founding team developed YOLO, a leading open source object detection model used in real world applications. We use a proprietary, high performance, binarized version of YOLO in our models for enterprise customers.
Too good to be true? Seems that they're running YOLO on conventional multi-core CPUs. On ARM even.
Everyone was pretty impressed. I'm always impressed when I see live demos go (almost) flawlessly.
The idea is to add an extra 2 params to the output of each classifier cell. Then do L2 normalization on them ( https://github.com/indutny/resistenz/blob/master/python/mode... ) and treat them as a cosine/sine pair.
The loss in this case would be the Euclidean distance between the actual and predicted pairs, which is equal to "2 * (1 - cos(x-y))".
I understand the benefits (as mentioned); would be interesting to know what disadvantages this has compared to the classifier type detection methods?
I've ignored mask RCNN becuase it's significantly more time consuming to label your data.
The main candidates are all found in Facebook’s Detectron package, but they didn't feel it necessary to document anything in any significant level of detail: https://github.com/facebookresearch/Detectron
You can see also: https://paperswithcode.com/sota/object-detection-coco