 Like, we have a session with our friends on this session, which depends on the level of activity. The output is, it's a white class activity. Like, the first example is red. It says that it is not updated. The green cards are updated. So, we have an input page like this. The output is just a green kill image where we can use as much as we want to gain what are the classes that we want. And if he's only getting all the things... What is the difference between these? These are all the same. We have a difference. What is the difference between these? These are all the same. And these are all the same. which is partly part of the project of the 15th divided to overall the success. This is also the case in the heritage, so we have to process the link to the play in our case. In this case, we have a number of parts, so we know the link. We need to air-watch them to get them every time they need to sort of mate them. We need to make a region more than not mate them. So we have a number of issues. We have this for two cases. Our next role is to detect how much it would like to mate. We also try to make it real-time, so the numbers should work to the last minute. But it takes the same time when the new project is happening. We get these eight sets, which are in this hyperspectral image, and they are all mixed with the music that they gave us, and they, not that these regions are up to any kind of time. It's a white classification because these hyperspectral images even get to the natives that are born. They keep going to the group, but CSC doesn't. We use the super pixels in the field of graph movement. So if we have a graph, we use the graph itself. It's a different picture of the very important image. We use that in our case. These three views that are present are not as common as in CSC. The interesting thing is that we train on the same image and test on the same image. So what we do is we take each percent of the image itself, train it, and then test all the images. This is the approach of hyperspectral image classification. We usually do all five of those in the landscape, which is a random image. We random it because if we don't random it, then we can only tell it's not as good as the picture. It's not as good as the real ones in CSC, so we don't randomly get both of them, and this is really hard to say. Because we have to get this image composite, and then use threshold to get only two cases of late to non-update. Then we use a classification of each pixel. Actually, the images are really high, so we think that we can use the formula, which doesn't exist before the process, and try to see if the data set is really deep, and quite hard to put together and see what will happen. So sometimes we have to work data sets, but not real data sets. So it's that real that we're creating to see what will happen next. What is the key? The key is how to efficient. It is often to demonstrate the cure for the loss, how much and between the late to the future, or essentially not the late to the future. I know that I thought it was bad overall information, but it wasn't. The final goal is to detect the late to the future, which should be only certain. But since we work out the data set, and ground through it, it generates random noise. But in real life, we don't want to generate that real, that noise. But since our inputs are mimicking the objects, they mimic this noise. But it should only get three circuits. So in this case, the key idea is that our algorithm learns from these inputs and what we want to generate seems to be the same as the last three. Because we want to have reliable ground. In this case, we don't have the actual ground. But we know that we have an algorithm which detects even noises, which is more difficult than just detecting the results. So for this, the algorithm knows the late to the future, the truth of the data set, if it is a late to the future, it detects the late to the future. And it is like this, that if it is not a late to the future, the model that predicts it is not a late to the future. And calculates these three metrics, which demonstrate that the important thing is that in the end, we choose a graph of pressure and then this is the height of the model.