im using darknet to train a logistic regresion model. but it always output the same prediction for different input image.
but when i remove some convolutional layers , it seems to become normal.(different output for different input images)
the model cfg file is as follows:
[net]
some parameter...
[convolutions]
[convolutions]
[shortcut]
...
[avgpool]
[connected]
batch_normalize=1
output=1
activation=linear
[logistic]
i tryed different learning rate, momentum. not work.
and the trainging data is ballanced. two class, 15000images for each class.
any advices?
thanks.
Related
I'm using caffe to do the object detection with SSD model, and recently work I adjust the loss type of "MultiBoxLoss".
In the multibox_loss_layer.cpp file, its loss has SOFTMAX as default and LOGISTIC option, I add the hingeloss(SVM) option into caffe code, and do the training but the result is bad.
Now the boss want me to use SVM to classify the feature map by python sklearn.
And a question come across to me, in the multibox_loss_layer.cpp file, there can use the softmax, logistic and hingeloss to calculate the loss. On this step, its data is just "one-dimension", but the feature map is high-dimension, and I internet the article, it seem softmax can't classify high-dimension data.
Ex: if there have three class: cat, dog and rabbit, then it's one-dimension data just have three value to represent cat, dog and rabbit(one value for each class), but the high-dimension data, it have many value(like feature map) for each class, and on the high-dimension case, softmax seems have no work for this.
so I wonder what's the different between softmax, logistic and SVM. Can anybody help? thank you!
Never seen applying SVM loss function into NN. However softmax is a loss function which should be used in order to optimize solution multiclass classifiaction problem. Softmax "transform" NN outputs into probability of each class occurance. Logistic function usually optimize each neuron output as a logistic problem, so it's not force output to be only one class. You should use this function if you want to solve multi labeling problem.
SVM is not a function, is a different classifier. There is no sense in comparing softmax with SVM, because first one is a loss function second one is a classifier.
I am trying to write a script which loads the camera parameters from Meshroom and imports them into a CAD program. My first understanding was that these parameters (position, rotation matrix, focal length etc.) are contained in the JSON-file cameras.sfm in the StructureFromMotion-subdirectory.
After importing these parameters into Rhino3D and comparing the resulting views onto the 3D-mesh with the undistorted photographs in the PrepareDenseScene-directory, I find surprising large discrepancies. The mesh which was the result of the run was good, so I think that the deviation is because of the parameters in cameras.sfm being not the final ones. This assumption is also supported by the fact that the file only contains the focal length as read from the input images' EXIF information and no refined values. So my question is:
How can I access the final camera parameters from the output of Meshroom?
Knowing this would help me a lot for re-building a photogrammetry/CAD pipeline I had previously implemented for VisualSFM + CMPMVS.
Many thanks!
EDIT: As this is my first post, I am not able to create a new tag for Meshroom. Perhaps this could be added by someone else? Thanks!
I'm writing a method to parse the data in wavefront obj files and I understand the format for the most part, however some things are still a bit confusing to me. For instance, I would have expected most files to list all the vertices first, followed by the texture and normal map coordinates and then the face indices. However, some files that I have opened alternate between these different sections. For instance, one .obj file I have of the Venus de Milo (obtained here: http://graphics.im.ntu.edu.tw/~robin/courses/cg03/model/ ) starts off with the vertices (v), then does normal coordinates (vn), then faces (f), then defines more vertices, normals and faces again. Why is the file broken up into two sections like this? Why not list all the vertices up front? Is this meant to signify that there are multiple segments to the mesh? If so, how do I deal with this?
Because this is how the file format was designed. There is no requirement for a specific ordering of the data inside the OBJ, so each modelling package writes it in its own way. Here is one brief summary of the file format, if you haven't read this one yet.
That said, the OBJ format is quite outdated and doesn't support animation by default. It is useful for exchanging of static meshes between modelling tools but not much else. If you need a more robust and modern file format, I'd suggest taking a look at the Collada format or the FBX.
not an direct answer but it will be unreadable in comment
I do not use this file-format but mesh segmentation is usually done for these reasons:
more easy management of the model for editing
separation of parts of model with different material or texture properties
mainly to speed up the rendering by cut down unnecessary material or texture switching
if the mesh has dynamically moving parts then they must be separated
Most 3D mesh file formats contains also transform matrix for each mesh part and some even an skeleton hierarchy
Now how to handle segmented meshes:
if your engine supports only unsegmented models then merge all parts together
This will loose all the advantages of segmented mesh. Do not forget to apply transform matrices of sub segments before merging
or you can implement mesh segmentation into your model class
By adding model hierarchy , transform matrices , ...
Now how to handle mixed model fileformat:
scan file for all necessary chunks of data
remember if they are present
also store their size,and start address in file
and do not forget that there may be more that one chunk of the same data type
preallocate space for all data you need
load/merge all data you need
load chunks of data to you model classes or merge it to single model
of course check if all data needed id present like number of points match number of normals or texture coords ...
I'm trying to write a neural network that (after being properly trained) identifies certain road signs and returns a different output for each type of sign.
Before I started to train my network, I noticed on the pybrain website that their datasets are always an array of values, each entry containing an input and a target. The images I have for my NN have been converted to grayscale pixel data (a simple array of numbers). To train each set of data, do I need to somehow add a target value for each pixel? And if so, how would I go about doing that?
QUICK ANSWER
No, you don't need target for every single pixel, you treat pixels from single image as your input data and you add target to that data.
LONG ANSWER
What you trying to do is to solve classification problem. You have image represented by array of numbers and you need to classify it as some class from limited set of classes.
So lets say that you have 2 classes: prohibitions signs (I'm not native speaker, I don't know how you call signs that forbid something), and information signs. Lets say that prohibition signs is our class 1 and information signs is class 2.
Your data set should look like this:
([representation of sign in numbers], class) - single sample
After that, since it's classification problem, I recommend using _convertToOneOfMany() method of DataSet class, to convert your targets into multiple outputs.
I've answered similar question here, go check it out.
I'm interested in different algorithms people use to visualise millions of particles in a box. I know you can use Cloud-In-Cell, adaptive mesh, Kernel smoothing, nearest grid point methods etc to reduce the load in memory but there is very little documentation on how to do these things online.
i.e. I have array with:
x,y,z
1,2,3
4,5,6
6,7,8
xi,yi,zi
for i = 100 million for example. I don't want a package like Mayavi/Paraview to do it, I want to code this myself then load the decomposed matrix into Mayavi (rather than on-the-fly rendering) My poor 8Gb Macbook explodes if I try and use the particle positions. Any tutorials would be appreciated.
Analysing and creating visualisations for complex multi-dimensional data is complex. The best visualisation almost always depends on what the data is, and what relationships exists within the data. Of course, you are probably wanting to create visualisation of the data to show and explore relationships. Ultimately, this comes down to trying different posibilities.
My advice is to think about the data, and try to find sensible ways to slice up the dimensions. 3D plots, like surface plots or voxel renderings may be what you want. Personally, I prefer trying to find 2D representations, because they are easier to understand and to communicate to other people. Contour plots are great because they show 3D information in a 2D form. You can show a sequence of contour plots side by side, or in a timelapse to add a fourth dimension. There are also creative ways to use colour to add dimensions, while keeping the visualisation comprehensible -- which is the most important thing.
I see you want to write the code yourself. I understand that. Doing so will take a non-trivial effort, and afterwards, you might not have an effective visualisation. My advice is this: use a tool to help you prototype visualisations first! I've used gnuplot with some success, although I'm sure there are other options.
Once you have a good handle on the data, and how to communicate what it means, then you will be well positioned to code a good visualisation.
UPDATE
I'll offer a suggestion for the data you have described. It sounds as though you want/need a point density map. These are popular in geographical information systems, but have other uses. I haven't used one before, but the basic idea is to use a function to enstimate the density in a 3D space. The density becomes the fourth dimension. Something relatively simple, like the equation below, may be good enough.
The point density map might be easier to slice, summarise and render than the raw particle data.
The data I have analysed has been of a different nature, so I have not used this particular method before. Hopefully it proves helpful.
PS. I've just seen your comment below, and I'm not sure that this information will help you with that. However, I am posting my update anyway, just in case it is useful information.