I'm about to start developing a neural net here with Tensorflow, but before I get into it too deep, I was hoping I could get some feedback on exactly what type of neural net I will need for this (If a net is the right way to go about this at all)
I need the NN to input an image, and output another image. This will be used for path-mapping on a robot I'm working on. The input image will be a disparity map, and the output will be a "driveable map" (an image that displays what in the scene can be driven on, and what can't)
I have built a dataset using Unity 3d. Here is an example from the set:
disparity map
driveable map:
As you can probably see, white represents the area where my robot can drive and black is where it can't. I will need the NN to take a disparity map, and give me back a "driveable map". Can this be done?
Thanks!
Sorry I'm not an expert. Since there hasn't been a response on this and if you are still looking, the vocabulary I would use to describe this type of problem is disparity networks and segmentation. Your best bet may be a specific type of disparity network: U-net
Related
I am working on a project where I need the app to recognize a special character, just one small graphic, from a photographed document. Something similar to the example from the picture. More specifically, the app would use this character to determine the corners of the document.
Something like this
Which model would be suitable for that, Mobile SSD, Yolo or something completely different? Approximately how many photos and how much time would it take me to successfully train the model to 90%+ detection? And is TensorFlow Model Maker a good option?
I already tried to train it with Model Maker but the results were really disappointing. I have used
efficientdet_lite0
model. The photos were taken with a phone in high resolution, tagged with labelImg. About 40 for training, five each for validation and test.
It would mean a lot to me if someone would tell me if I am at least on the right track. Thank you very much in advance.
efficientdet or yolo should be good enough for you use case. Yolov5 recommends 10000 annotated instances of each class for good results, this is given that the variability of the class representation is large. I would not start with anything less than a few hundred.
[![enter image description here][1]][1]I am actually reconstructing some images using dual photography. Next, I want to train a network to reconstruct clear images by removing noise (Denoising autoencoder).
The input for training the network is reconstructed images, whereas, the output is ground truth or computer based standard test images. Now the input e.g., Lena is some how not exact version of Lena with image shifted in positions and some artifacts.
If I keep input as my reconstructed image and training output as Lena test image (computer standard test image) , will it work?
I only want to know if input/output shifted or some details missing in one of them (due to some cropping) would work.
It depends on many factors like your images for training and the architecture of the network.
However, what you want to do is to make a network that learns the noise or low level information and for this purpose Generative Adversarial Networks (GAN) are very popular. You can read about them here. Maybe, after you have tried your approach and if the results are not satisfactory then try using GANs, like, DCGAN (Deep Convolution GAN).
Also, share your outcomes with the community if you would like.
Denoising Autoencoders! Love it!
There is no reason for not training your model with those images. The autoencoder, if well trained, will eventually learn the transformation if there is enough data.
However, if you have the 'positive' images, I strongly recommend you to create your own noisy images and then train in that controlled working area. You will simplify your problem and it will be easier to solve.
What is stopping you from doing just that?
I'm using cnn built by keras(tensorflow) to do visual recognition.
I wonder if there is a way to know what my own tensorflow model "see".
Google had a news showing the cat face in the AI brain.
https://www.smithsonianmag.com/innovation/one-step-closer-to-a-brain-79159265/
Can anybody tell me how to take out the image in my own cnn networks.
For example, what my own cnn model recognize a car?
We have to distinguish between what Tensorflow actually see:
As we go deeper into the network, the feature maps look less like the
original image and more like an abstract representation of it. As you
can see in block3_conv1 the cat is somewhat visible, but after that it
becomes unrecognizable. The reason is that deeper feature maps encode
high level concepts like “cat nose” or “dog ear” while lower level
feature maps detect simple edges and shapes. That’s why deeper feature
maps contain less information about the image and more about the class
of the image. They still encode useful features, but they are less
visually interpretable by us.
and what we can reconstruct from it as a result of some kind of reverse deconvolution (which is not a real math deconvolution in fact) process.
To answer to your real question, there is a lot of good example solution out there, one you can study it with success: Visualizing output of convolutional layer in tensorflow.
When you are building a model to perform visual recognition, you actually give it similar kinds of labelled data or pictures in this case to it to recognize so that it can modify its weights according to the training data. If you wish to build a model that can recognize a car, you have to perform training on a large train data containing labelled pictures. This type of recognition is basically a categorical recognition.
You can experiment with the MNIST dataset which provides with a dataset of pictures of digits for image recognition.
I am new to machine learning field and based on what I have seen on youtube and read on internet I conjectured that it might be possible to count pedestrians in a video using tensorflow's object detection API.
Consequently, I did some research on tensorflow and read documentation about how to install tensorflow and then finally downloaded tensorflow and installed it. Using the sample files provided on github I adapted the code related to object_detection notebook provided here ->https://github.com/tensorflow/models/tree/master/research/object_detection.
I executed the adapted code on the videos that I collected while making changes to visualization_utils.py script so as to report number of objects that cross a defined region of interest on the screen. That is I collected bounding boxes dimensions (left,right,top, bottom) of person class and counted all the detection's that crossed the defined region of interest (imagine a set of two virtual vertical lines on video frame with left and right pixel value and then comparing detected bounding box's left & right values with predefined values). However, when I use this procedure I am missing on lot of pedestrians even though they are detected by the program. That is the program correctly classifies them as persons but sometimes they don't meet the criteria that I defined for counting and as such they are not counted. I want to know if there is a better way of counting unique pedestrians using the code rather than using the simplistic method that I am trying to develop. Is the approach that I am using the right one ? Could there be other better approaches ? Would appreciate any kind of help.
Please go easy on me as I am not a machine learning expert and just a novice.
You are using a pretrained model which is trained to identify people in general. I think you're saying that some people are pedestrians whereas some other people are not pedestrians, for example, someone standing waiting at the light is a pedestrian, but someone standing in their garden behind the street is not a pedestrian.
If I'm right, then you've reached the limitations of what you'll get with this model and you will probably have to train a model yourself to do what you want.
Since you're new to ML building your own dataset and training your own model probably sounds like a tall order, there's a learning curve to be sure. So I'll suggest the easiest way forward. That is, use the object detection model to identify people, then train a new binary classification model (about the easiest model to train) to identify if a particular person is a pedestrian or not (you will create a dataset of images and 1/0 values to identify them as pedestrian or not). I suggest this because a boolean classification model is about as easy a model as you can get and there are dozens of tutorials you can follow. Here's a good one:
https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network.ipynb
A few things to note when doing this:
When you build your dataset you will want a set of images, at least a few thousand along with the 1/0 classification for each (pedestrian or not pedestrian).
You will get much better results if you start with a model that is pretrained on imagenet than if you train it from scratch (though this might be a reasonable step-2 as it's an extra task). Especially if you only have a few thousand images to train it on.
Since your images will have multiple people in it you have a problem of identifying which person you want the model to classify as a pedestrian or not. There's no one right way to do this necessarily. If you have a yellow box surrounding the person the network may be successful in learning this notation. Another valid approach might be to remove the other people that were detected in the image by deleting them and leaving that area black. Centering on the target person may also be a reasonable approach.
My last bullet-point illustrates a problem with the idea as it's I've proposed it. The best solution would be to alter the object detection network to ouput both a bounding box per-person, and also a pedestrian/non pedestrian classification with it; or to only train the model to identify pedestrians, specifically, in the first place. I mention this as more optimal, but I consider it a more advanced task than my first suggestion, and a more complex dataset to manage. It's probably not the first thing you want to tackle as you learn your way around ML.
My problem statement is as follows :
" Object Detection and Localization using Tensorflow and convolutional neural network "
What i did ?
I am done with the cat detection from images using tflearn library.I successfully trained a model using 25000 images of cats and its working fine with good accuracy.
Current Result :
What i wanted to do?
If my image consist of two or more than two objects in the same image for example cat and dog together so my result should be 'cat and dog' and apart from this i have to find the exact location of these two objects on the image(bounding box)
I came across many high level libraries like darknet , SSD but not able to get the concept behind it.
Please guide me about the approach to solve the problem.
Note : I am using supervised learning techniques.
Expected Result :
You have several ways to go about it.
The most straight forward way is to get some suggested bounding boxes using some bounding box suggestion algorithm like selective search and run on each on of the suggestion the classification net that you already trained. This approach is the approach taken by R-CNN.
For more advanced algorithm based on the above approach i suggest you read about Fast-R-CNN and Faster R-CNN.
Look at Object detection with R-CNN? for some basic explanation.
Darknet and SSD are based on a different approach if you want to undestand them you can read about them on
http://www.cs.unc.edu/~wliu/papers/ssd.pdf
https://pjreddie.com/media/files/papers/yolo.pdf
Image localization is a complex problem with many different implementations achieving the same result with different efficiency.
There are 2 main types of implementation
-Localize objects with regression
-Single Shot Detectors
Read this https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/object_localization_and_detection.html to get a better idea.
Cheers
I have done a similar project (detection + localization) on Indian Currencies using PyTorch and ResNet34. Following is the link of my kaggle notebook, hope you find it helpful. I have manually collected images from the internet and made bounding box around them and saved their annotation file (Pascal VOC) using "LabelImg" annotation tool.
https://www.kaggle.com/shweta2407/objectdetection-on-custom-dataset-resnet34