I am working on a project for face recognition with photos taken by cameras. I should use a virtual machine with spark and deeplearning4j.
The problem is that I didn't find the suitable algorithm and code to use for creating the neural network.
What is the difference between VGG16, keras, dataVec? and when we should use those models?
All the things you asked can be found through just a google search still giving you links just to give you a direction
VGG16
Keras
Related
I have downloaded this pre-trained model "model_ir_se50.pth" for face recognition. It is giving very good results. How can i know its architecture?
simple print(net) #replace net with the network variable will work as per your need.
I'm going to implement a translator based on NMT(Neural Machine Translation). In here I hope to use only monolingual corpora without using parallel corpus data for my dataset. Is it possible to train the model using only monolingual corpora data? I'm grateful if someone can share your idea regarding this.
I´m new here, so please be kind and teach me if I did not provide all the information you need :)
I would like to compare Edge TPU with other edge device such as Myriad. I would like to select one object detection model and one image segmentation model. Considering the following link which shows supported operations, I have noticed that yolov3 cannot be compiled for EdgeTPU because it includes LeakyRelu.
https://coral.withgoogle.com/docs/edgetpu/models-intro/
For image segmentation, I'd like to use Deeplab. But I'm still don't know if operations included in deeplab v3+, such as atrous convolution or feature pyramid network, are supported.
I'd appreciate if someone teach me what models are usable on edgeTPU. Are there any models of image segmentation?
Did you already found below?
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/quantize.md
"mobilenetv2_coco_voc_trainaug_8bit":
deeplabv3_mnv2_pascal_train_aug_8bit/frozen_inference_graph.pb
This model is possible to converting to TFLite FlatBuffer.
And also possible to compile for edgetpu with edgetpu_compiler.
Note. edgetpu_api environment had updated.
You can find it below.
https://coral.withgoogle.com/news/updates-07-2019/
Yes. There are prepackaged segmentation models and code examples how to use them.
Here they are https://coral.ai/models/
Please share if you know where to find something similar for Movidius based VPU devices.
Here you can find all supported layers for edgetpu: https://coral.ai/docs/edgetpu/models-intro/#supported-operations.
And for Conv2D it says "Must use the same dilation in x and y dimensions.". So implementing a version of deeplab v3+ is possible for the edgetpu.
I have installed darknet in ubuntu and is now trying to implement object detection using yolo v2 on my custom dataset. In the yolo paper, they have told that they have pretrained the network using image net dataset. So, my question is should we also pretrain the network?
Sorry if I'm blunting.
Can someone reply me?
For most cases, if your dataset has lots of similar feature in the pre-trained weight (e.g. person, car), you should use the pre-trained network such as darknet53.conv.74 or darknet19_448.conv.23.
But you can also train the network without using those pre-trained network (training from scratch), for example by removing the weight from the command :
./darknet detector train data/obj.data yolo-obj.cfg
I'm new in the field of Deep Neural Network. There are various deep learning frameworks nearby. Notably Theano, Torch7, Caffe, and recently open sourced TensorFlow. I have tried out a couple of tutorials with TensorFlow provided on their site. Specifically the MNIST dataset. I guess this is the hello world of every deep learning framework out there. I also viewed tutorials from here. This one was explained in detail, but they do not provide hands on experience with any deep learning frameworks. So which framework should be better for beginners? I looked up similar questions asked on Quora. Some said that theano is tougher to learn but it gives more control, Caffe is easier, but it gives less control over the network. And nothing on Tensorflow, as it is new, but from what i've seen the documentation is not That well written, also it seems tougher to understand. So as a newbie what should i choose to learn?
Another question, As I said, MNIST is the hello world of every deep learning framework, and many neural networks can be found for recognizing MNIST dataset. So, if I use the same network to detect other dataset, say CIFAR-10 dataset, will it work?? Let's just say that i turn the CIFAR-10 dataset to grayscale images and convert it to same dimension as MNIST dataset. Will the model be invalid or fail to learn? or have bad accuracy or what?