I am implementing a keypoint detection algorithm to recognize biomedical landmarks on images. I only have one type of landmark to detect. But in a single image, 1-10 of these landmarks can be present. I'm wondering what's the best way to organize the ground truth to maximize learning.
I considered creating 10 landmark coordinates per image and associate them with flags that are either 0 (not present) or 1 (present). But this doesn't seem ideal. Since the multiple landmarks in a single picture are actually the same type of biomedical element, the neural network shouldn't be trying to learn them as separate entities.
Any suggestions?
One landmark that can appear everywhere sounds like a typical CNN problem. Your CNN filters should learn which features make up the landmark, but they don't care where it appears. That would be the responsibility of the next layers. Hence, for training the CNN layers you can use a monochrome image as the target: 1 is "landmark at this pixel", 0 if not.
The next layers are basically processing the CNN-detected features. To train those, your ground truth should be basically the desired outcome. Do you just need a binary output (count>0)? A somewhat accurate estimate of the count? Coordinates? Orientation? NN's don't care that much what they learn, so just give it in training what it should produce in inference.
After YOLO1 there was a trend of using anchor boxes for a while in other iterations as priors (I believe the reason was to both speed up the training and detect different sized objects better)
However YOLOV1 has an interesting mechanism where there are k number of bounding box predictors sliding each grid cell in order to be able to specialize in detecting different scaled objects.
Here is what I wonder, ladies and gentlemen:
Given a very long training time, can these bounding box predictors in YOLOV1 achieve better bounding boxes compared to YOLOV9000 or its counterparts that rely on anchor box mechanism
In my experience, yes they can. I observed two possible optimization paths, one of which is already implemented in latest version of YOLOV3 and V5 by Ultralytics (https://github.com/ultralytics/yolov5)
What I observed was that for a YOLOv3, even before training, using a K means clustering we can ascertain a number of ``common box'' shapes. These data when fed into the network as anchor maskes really improved the performance of the YOLOv3 network for "that particular" dataset since the non-max suppression routine had much better chance of succeeding at filtering out spurious detection for particular classes in each of the detection head. To the best of my knowledge, this technique was implemented in latest iterations of their bounding box regression code.
Suppressing certain layers. In YOLOv3, the network performed detection in three stages with the idea of progressively detecting larger objects to smaller objects. YOLOv3 (and in theory V1) can benefit if with some trial and error, you can ascertain which detection head is your network preferring to use based on the common bounding box shapes that you found in step 1.
I want to retrain the object detector Yolov4 to recognize figures of the board game Ticket to Ride.
While gathering pictures i was searching for an idea to reduce the amount of needed pictures.
I was wondering if more instances of an object/class in a picture means more "training per picture" which leads to "i need less pictures"
Is this correct? If not could you try to explain in simple terms?
On the roboflow page, they say that the YOLOv4 breaks detecting objects into two pieces:
regression to identify object positioning via bounding boxes;
classification to classify the objects into classes.
Regression (analysis) is - in short - a method of analysis that tries to find the data (images in your case) that is relevant. Classification - on the other hand - transforms the ‘interesting’ images from the previous step into a class (which is ’train piece’, ’tracks’, ’station’ or something else that is worth separating from the rest).
Now, to answer your question: “no, you need more pictures.” When taking more pictures, YOLOv4 is using more samples make / test a more accurate classification. Yet, you have to be careful what you want to classify. You do want the algorithm to extract a ’train’ class from an image, but not an ‘ocean’ class for example. To prevent this, make more (different) pictures of the classes you want to have!
I'm attempting to write a short mini-program in Python that plays around with force-based algorithms for graph drawing.
I'm trying to minimize the number of times lines intersect. Wikipedia suggests giving the lines an electrical charge so that they repel each other. I asked my physics teacher how I might simulate this, and she mentioned using calculus with Coulomb's Law, but I'm uncertain how to start.
Could somebody give me a hint on how I could do this? (Or alternatively, another way to tweak a force-based graph drawing algorithm to minimize the number of times the lines cross?) I'm just looking for a hint; no source code please.
In case anybody's interested, my source code and a youtube vid I made about it.
You need to explicitly include a term in your cost function that minimizes the number of edge crossings. For example, for every pair of edges that cross, you incur a fixed penalty or, if the edges are weighted, you incur a penalty that is the product of the two weights.
Does anyone have any good references for equations which can be implemented relatively easily for how to compute the transfer of angular momentum between two rigid bodies?
I've been searching for this sort of thing for a while, and I haven't found any particularly comprehensible explanations of the problem.
To be precise, the question comes about as this; two rigid bodies are moving on a frictionless (well, nearly) surface; think of it as air hockey. The two rigid bodies come into contact, and then move away. Now, without considering angular momentum, the equations are relatively simple; the problem becomes, what happens with the transfer of angular momentum between the bodies?
As an example, assume the two bodies have no angular momentum whatsoever; they're not rotating. When they interact at an oblique angle (vector of travel does not align with the line of their centers of mass), obviously a certain amount of their momentum gets transferred into angular momentum (i.e. they each get a certain amount of spin), but how much and what are the equations for such?
This can probably be solved by using a many-body rigid system to calculate, but I want to get a much more optimized calculation going, so I can calculate this stuff in real-time. Does anyone have any ideas on the equations, or pointers to open-source implementations of these calculations for inclusion in a project? To be precise, I need this to be a rather well-optimized calculation, because of the number of interactions that need to be simulated within a single "tick" of the simulation.
Edit: Okay, it looks like there's not a lot of precise information about this topic out there. And I find the "Physics for Programmers" type of books to be a bit too... dumbed down to really get; I don't want code implementation of an algorithm; I want to figure out (or at least have sketched out for me) the algorithm. Only in that way can I properly optimize it for my needs. Does anyone have any mathematic references on this sort of topic?
If you're interested in rotating non-spherical bodies then http://www.myphysicslab.com/collision.html shows how to do it. The asymmetry of the bodies means that the normal contact force during the collision can create a torque about their respective CGs, and thus cause the bodies to start spinning.
In the case of a billiard ball or air hockey puck, things are a bit more subtle. Since the body is spherical/circular, the normal force is always right through the CG, so there's no torque. However, the normal force is not the only force. There's also a friction force that is tangential to the contact normal which will create a torque about the CG. The magnitude of the friction force is proportional to the normal force and the coefficient of friction, and opposite the direction of relative motion. Its direction is opposing the relative motion of the objects at their contact point.
Well, my favorite physics book is Halliday and Resnick. I never ever feel like that book is dumbing down anything for me (the dumb is inside the skull, not on the page...).
If you set up a thought problem, you can start to get a feeling for how this would play out.
Imagine that your two rigid air hockey pucks are frictionless on the bottom but have a maximal coefficient of friction around the edges. Clearly, if the two pucks head towards each other with identical kinetic energy, they will collide perfectly elastically and head back in opposite directions.
However, if their centers are offset by 2*radius - epsilon, they'll just barely touch at one point on the perimeter. If they had an incredibly high coefficient of friction around the edge, you can imagine that all of their energy would be transferred into rotation. There would have to be a separation after the impact, of course, or they'd immediately stop their own rotations as they stuck together.
So, if you're just looking for something plausible and interesting looking (ala game physics), I'd say that you could normalize the coefficient of friction to account for the tiny contact area between the two bodies (pick something that looks interesting) and use the sin of the angle between the path of the bodies and the impact point. Straight on, you'd get a bounce, 45 degrees would give you bounce and spin, 90 degrees offset would give you maximal spin and least bounce.
Obviously, none of the above is an accurate simulation. It should be a simple enough framework to cause interesting behaviors to happen, though.
EDIT: Okay, I came up with another interesting example that is perhaps more telling.
Imagine a single disk (as above) moving towards a motionless, rigid, near one-dimensional pin tip that provides the previous high friction but low stickiness. If the disk passes at a distance that it just kisses the edge, you can imagine that a fraction of its linear energy will be converted to rotational energy.
However, one thing you know for certain is that there is a maximum rotational energy after this touch: the disk cannot end up spinning at such a speed that it's outer edge is moving at a speed higher than the original linear speed. So, if the disk was moving at one meter per second, it can't end up in a situation where its outer edge is moving at more than one meter per second.
So, now that we have a long essay, there are a few straightforward concepts that should aid intuition:
The sine of the angle of the impact will affect the resulting rotation.
The linear energy will determine the maximum possible rotational energy.
A single parameter can simulate the relevant coefficients of friction to the point of being interesting to look at in simulation.
You should have a look at Physics for Game Developers - it's hard to go wrong with an O'Reilly book.
Unless you have an excellent reason for reinventing the wheel,
I'd suggest taking a good look at the source code of some open source physics engines, like Open Dynamics Engine or Bullet. Efficient algorithms in this area are an artform, and the best implementations no doubt are found in the wild, in throroughly peer-reviewed projects like these.
Please have a look at this references!
If you want to go really into Mecanics, this is the way to go, and its the correct and mathematically proper way!
Glocker Ch., Set-Valued Force Laws: Dynamics of Non-Smooth Systems. Lecture Notes in Applied Mechanics 1, Springer Verlag, Berlin, Heidelberg 2001, 222 pages. PDF (Contents, 149 kB)
Pfeiffer F., Glocker Ch., Multibody Dynamics with Unilateral Contacts. JohnWiley & Sons, New York 1996, 317 pages. PDF (Contents, 398 kB)
Glocker Ch., Dynamik von Starrkörpersystemen mit Reibung und Stößen. VDI-Fortschrittberichte Mechanik/Bruchmechanik, Reihe 18, Nr. 182, VDI-Verlag, Düsseldorf, 1995, 220 pages. PDF (4094 kB)