I am looking for a suitable camera for my iris recognition system but the prices of some of the cameras are steep, i stumbled on the openmv m7 which seems promising but the max resolution is 640x480 8 bit greyscale. The camera needs to be 20cm from the user.Please help!
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I have a small Dataset of ~500 images with a relatively high resolution of 800x600 pixels.
I got the impression that prebuild models are often made for big datasets with small images.
Currently I use Efficientnet B0 to classify those images using 800x600 pixels as input. This violates the compound scaling idea of Efficientnet. Bigger versions like B7 tend to overfit.
Does anyone know a better approach? Maybe an architecture that is suited for small datasets?
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/
So I have a laptop that has GTX1050ti and the cpu is i7 7700hq and I'm very curious about which version should I choose so it can fit the performance of my laptop and also can YOLO predict image or just real-time detection?
I am currently using Lenovo Ideapad PC with AMD Radeon graphics in it. I am trying to run an image classifier model using convolutional neural networks. The dataset contains 50000 images and it takes too long to train the model. Can someone tell me how can I use my AMD GPU to fasten the process. I think AMD Graphics does not support CUDA. So is there any way around?
PS: I am using Ubuntu 17.10
What you're asking for is OpenCL support, or in more grandiose terms: the democratization of accelerated devices. There seems to be tentative support for OpenCL, I see some people testing it as of early 2018, but it doesn't appear fully baked yet. The issue has been tracked for quite some time here:
https://github.com/tensorflow/tensorflow/issues/22
You should also be aware of development on XLA, an attempt to virtualize tensorflow over an LLVM (or LLVM-like) virtualization layer making it more portable. It's currently cited as being in alpha as of early 2018.
https://www.tensorflow.org/performance/xla/
There isn't yet a simple solution, but these are the two efforts to follow along these lines.
I'd like to make app for recognition persons in video stream using tensorflow or keras.
What kind of neural network can i use? CNN or RNN? Shoud i analyze freame one by one or video stream as a whole? Any good source to learn?
This is a very large question. And a hard task.
I think, the easy way is to extract one frame by second if the source is a video stream.
Then use OpenCV to make a face detection.
Once you got the faces, feed a NN for recognitions.
Some links for face recognition in Deep Learning:
https://aboveintelligent.com/face-recognition-with-keras-and-opencv-2baf2a83b799
https://github.com/rajathkumarmp/FaceRecog-Keras/blob/master/faceRecog.ipynb