I'm looking for a way to detect parts of a face (like eyes, nose, lips) with Tensorflow Lite. So far, I haven't seen much info. Is there a way to actually retrieve coordinates that describe this kind of data?
Thanks
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I successfully trained multi-classificator model, that was really easy with simple class related folder structure and keras.preprocessing.image.ImageDataGenerator with flow_from_directory (no one-hot encoding by hand btw!) after i just compile fit and evaluate - extremely well done pipeline by Keras!
BUT! when i decided to make my own (not cats, not dogs, not you_named) object detector - this is became a nightmare...
TFRecord and tf.Example are just madness! but ok, i almost get it (my dataset is small, i have plenty of ram, but who cares, write f. boilerplate, so much meh...)
The main thing - i just can't find any docs/tutorial how to make it with plain simple tf/keras, everyone just want to build up it on top of someone model, YOLO SSD FRCNN, even if they trying to detect completely new objects!!!
There two links about OD in official docs, and they both using some models underneath.
So my main question WHY ??? or i just blind..? -__-
It becomes a nightmare because Object Detection is way way harder than classification. The most simple object detector is this: first train a classifier on all your objects. Then when you want to detect objects in your image, slide a window over your image, and classify each window. Then, if your classifier is certain that a certain window is one of the objects, mark it as a successful detection.
But this approach has a lot of problems, mainly it's way (like waaaay) too slow. So, researcher improved it and invented RCNNs. That had it problems, so they invented Faster-RCNN, YOLO and SSD, all to make it faster and more accurate.
You won't find any tutorials online on how to implement the sliding window technique because it's not useful anyway, and you won't find any tutorials on how to implement the more advanced stuff because, well, the networks get complicated pretty quick.
Also note that using YOLO doesn't mean you should use the same weights as in YOLO. You can always train YOLO from scratch on your own data if you want by randomly initiliazing all the weights in the network layers. So the even if they trying to detect completely new objects!!! you mentioned isn't really valid. Also also note that I still would advise you to do use the weights they used in Yolo network. Transfer Learning is generally looked at as being a good idea, especially when starting out and especially in the image processing world, as many images share common features (like edges, for example).
I am having pretty much the same problem as my images are B/W diagrams, quite different from regular pictures, I want to train a custom model on just only diagrams.
I have found this documentation section in Tensorflow models repo:
https://github.com/tensorflow/models/blob/master/research/object_detection/README.md
It has a couple of sections explaining how to bring your own model and dataset in "extras" that could be a starting point.
I have images of unique products that are used at my workplace. I can't imagine that the inception database already has similar items that it has been trained on.
I tried to train a model using YOLO. It was taking a very very long time. Maybe 7minutes between epochs; and I wanted to do 1000 epochs due to small data size.
I used tiny-yolov2-voc cfg/weight on 1.0 GPU. I had a video of the item but i broke it up into frames so i could annotate. I then attempted to train on the images (not video). The products are healthcare related. Basically anything that a hospital would use.
Ive also used the inception method on images I got from Google. I noticed that inception method was very fast and resulted in accurate predictions. However, i'm worried that my images are too unique for inception to work.
Which method is best to use?
If you recommend YOLO, can you please provide suggestions on how to speed up the training phase?
If you recommend inception, can you please provide an explanation why it would work on unique images? I guess i'm having trouble understanding how inception knows which item i'm trying to train on without me providing annotations.
Thanks in advance
Just my impression (no recommendation or even related experience)
Having a look at the Hardware recommendations related to darknet a assumption is that you might stock up your own hardware to get faster results.
I read about the currently three different versions of YOLO and expect there are lot's of GFLOPS training included if you download the recommended files, but if the models never fit to your products then for you they never might be very helpful.
I must admit I've neither been active with YOLO nor with Tensorflow, so my impression might not be helpful at all.
If you see some videos of YOLO you can remark that sometimes a camel is labeled with horse and the accuracy seems being bad but it depends on the threshold that is applied to the images, so the videos look amazing as it seems the recognition is done so fast but with higher accuracy the process would slow down - also depending on the trained motives.
They never hide it though, they explain on an image where a dog is labeled as cow and a horse as sheep (Version 2) that in combination with darknet it's getting much faster but less accurate too, so usage of darknet is an important aspect too.
The information about details seems being quite bad on the websites of YOLO, they present it more like you'd do with a popstar, in comparison the website of Tensorflow looks more academic and is informing about the mathematics behind the framework.
Concerning Tensorflow I don't know about the hardware-recommendations, but as you wrote your results are useful, probably they are a bit or even much less.
My impression is that YOLO is primary intended for real-time detection in (live-)videos and needs much training for high accuracy. So depending on your use-case it might be right but you'd to invest in hardware probably for professional usage.
This is not an opinion against Tensorflow but that I had to verify more and it seems taking more time to get an impression. Concerning Tensorflow in the moment I even can't say if it can be used for real-time-detection, how accurate it is then and if the results are then still better then those of YOLO.
My assumption is that concerning both solutions it's a matter of involved elements (like the decision if to include darknet for speed), configuration, training and adjustments. Probably there is always something to increase in speed and accuracy, so investing in a system for recognition won't be static process with fixed end in timeline, but a steady process.
This is just a short overview of my impressions, I've never any experience with any recognition-software and hardly recommend that you make any decision based on my words.
Just if you want to do use any recognition software professional, especially for real-time-recognition, then you've to invest in hardware probably.
To my understanding of your problem you need you need inception with the capability of identifying your unique images. In this circumstance you can use transfer-learning on the inception model. With transfer-learning you can still train inception your own pictures while retaining the previous knowledge of inception.
More on transfer-learning
I am new to both TensorFlow and also Document Similarity / Topic Modeling therefore I apologize if my questions don't make complete sense.
From my limited understanding, topic modelling is done using algorithms such as LSA,LDA,etc. I have seen code using gensim and LSA but the time to train is very high for the large set of documents I have in mind. Consequently the CPU and RAM resources are very heavy.
Tensorflow doesn't seem to have a native LSA or LDA implementation.
I would appreciate an opinion on :
Would LDA implemented using Tensorflow have a better performance than implemented using gensim?
Could someone tell me of other Tensorflow primitives that I should look at for document similarity rather than LDA?
Once again I am sorry if my questions are too vague and do not cover sufficient information to give a proper response. I am new to this domain and I would appreciate any directions someone could point me to.
Thank you for your time.
Regards,
Jeetu
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
This is a newbie question for the tensorflow experts:
I reading lot of data from power transformer connected to an array of solar panels using arduinos, my question is can I use tensorflow to predict the power generation in future.
I am completely new to tensorflow, if can point me to something similar I can start with that or any github repo which is doing similar predictive modeling.
Edit: Kyle pointed me to the MNIST data, which I believe is a Image Dataset. Again, not sure if tensorflow is the right computation library for this problem or does it only work on Image datasets?
thanks, Rajesh
Surely you can use tensorflow to solve your problem.
TensorFlow™ is an open source software library for numerical
computation using data flow graphs.
So it works not only on Image dataset but also others. Don't worry about this.
And about prediction, first you need to train a model(such as linear regression) on you dataset, then predict. The tutorial code can be found in tensorflow homepage .
Get your hand dirty, you will find it works on your dataset.
Good luck.
You can absolutely use TensorFlow to predict time series. There are plenty of examples out there, like this one. And this is a really interesting one on using RNN to predict basketball trajectories.
In general, TF is a very flexible platform for solving problems with machine learning. You can create any kind of network you can think of in it, and train that network to act as a model for your process. Depending on what kind of costs you define and how you train it, you can build a network to classify data into categories, predict a time series forward a number of steps, and other cool stuff.
There is, sadly, no short answer for how to do this, but that's just because the possibilities are endless! Have fun!