I want to use Raspberry pi for taking pictures and then process them through Tensorflow (train it to find that object in an image and count it) to count my objects for example. I couldn't find any example, do you know if this is possible? I know OpenCV maybe easier but do you know if this is possible with Tensorflow?
As far as I know you can't train a TensorFlow model on a Raspberry Pi, there simply isn't the processing power. However, you could train a TensorFlow model on a laptop/PC and then deploy the model on a Raspberry Pi to do object recognition.
Have a read of this blog post on Pyimagesearch, there are some really in depth tutorials on TensorFlow/Keras on Raspberry Pi.
https://www.pyimagesearch.com/2017/12/18/keras-deep-learning-raspberry-pi/
For future questions on SO try to have a go yourself and then post a question once you get stuck explaining what you've tried and any code you're using. You're more likely to learn more this way.
Related
I'm currently trying to train tiny yolo weights.
I've already trained normal yolov3 weights but I want to make a live detector on a raspberry pi so I need the tiny ones.
The training of the normal ones went great no hiccups whatsoever, but the tiny weights just won't work.
I've tried like 4 different tutorials but the outcome is the same everytime.
Google colab just stops.
I also tried to train the normals again to test but also there it immediately stops.
-clear 1 after the command doesn't work and I've tried to modify the cfg in different ways but nothing. I don't know what to do anymore. Does anyone have an idea or a tip. That would be great.
I am trying to develop a facial recognition system on a raspberry pi 4 for a university project. I have to use Google Auto ML, Facenet, and Tensorflow. I have some understanding of what they are (I think), just want some guidance on what each really does and how they affect each other's operation when it comes to facial recognition. Any guidance would really appreciate it, just need to be shown the right path that is all!
You can find a lot of articles in medium/Github/Youtube/instructables/Tensorflow Examples on deploying Face recognition in Raspberry pi as a blueprint to get a head start . But You have to play with your Raspberry pi a bit to gain some Ground skills if you are unaware of Hardware details and other skills like Capturing Frames from Video ,Training and evaluating data etc.
I see a stable wheel of Tensorflow wheel by PINTOO for installing Tensorflow in Raspberry pi. A USB accelerator is recommended to smoothen the computation process.You can also use our TFlite for Edge devices like Raspberry pi.
Once model is Trained , you can convert into smaller size Tensorflow lite models or use Rest-API to a server to get results.Post Queries here on SO When you find an obstacle.
Attaching below links for reference.
https://www.tensorflow.org/lite/examples
https://github.com/PINTO0309/Tensorflow-bin#usage
https://bhashkarkunal.medium.com/face-recognition-real-time-webcam-face-
recognition-system-using-deep-learning-algorithm-and-98cf8254def7
https://www.pyimagesearch.com/2018/06/25/raspberry-pi-face-recognition/
I'm in a group project in school and we are using the tensorflow object-detection API in a Raspberry Pi 3 but do not know how the object detection methods, SSD (single shot detector) and CNN (convolutional neural network), works underneath.
Can someone give a simple yet non-trivial explanation on how SSD and CNN works and recommendations on possible factors that might optimize the speed of the object detection methods.
Please link us to good articles if you know any!
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!
I have just started experimenting with Deep Learning and Computer Vision technologies. I came across this awesome tutorial. I have setup the TensorFlow environment using docker and trained my own sets of objects and it provided greater accuracy when I tested it out.
Now I want to make the same more real-time. For example, instead of giving an image of an object as the input, I want to utilize a webcam and make it recognize the object with the help of TensorFlow. Can you guys guide me with the right place to start with this work?
You may want to look at TensorFlow Serving so that you can decouple compute from sensors (and distribute the computation), or our C++ api. Beyond that, tensorflow was written emphasizing throughput rather than latency, so batch samples as much as you can. You don't need to run tensorflow at every frame, so input from a webcam should definitely be in the realm of possibilities. Making the network smaller, and buying better hardware are popular options.