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Can anyone please help me to distinguish between darknet and darkflow. Advantages of one over the other. My understanding about YOLO (You Only Look Once) is an algorithm for fast object detection.
Darknet is the name of the framework YOLO is originally implemented on.
Note DarkNet-XX (XX=19/51) is also the name of the backbone YOLO uses.
Darkflow is a nickname of an implementation of YOLO on TensorFlow.
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Is there some sort of software that can do so? Specifically, I would like to visualize Resnet18. Is there no other way other than to just draw it myself? Here is an example of what I want to see:
Sample Architecture Visualization
You can use this one : http://alexlenail.me/NN-SVG/LeNet.html . It lets you visualize neural networks by letting you modify several parameters and finally lets you export the architectures as SVG files. You can also choose between 3 visualization styles, namely FCNN, LeNet & AlexNet.
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I(x, y, no of channels) is the image, and Fi(x, y, no of filters ) is the feature map at some layer 'i'.
Given the architecture of a Convolutional Neural Network like VGGNet and a feature map after a certain layer Fi, is there an efficient way to find which pixels of the input image I, that contribute to a location in the feature map?
I will want to implement this in python.
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Does anybody know, how to transform classify_image_graph_def.pb from google's tensorflow for poets tutorial:
https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0
into
inception_v3_2016_08_28_frozen.pb utilised by Qualcomm in its SNPE SNPE tutorial:
https://developer.qualcomm.com/docs/snpe/tutorial_setup.html
?
When I retrain the classify_image_graph_def.pb, freeze it and use it as a model in the Qualcomm SNPE tutorial, then the app compiles OK but stops running on my Android device. (runtime error)
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I am new in the deep learning, and I want to use convolutional neural networks (CNN) for image recognition (biometric images).
I would like to use pre-trained CNN architecture and use a python programming language.
How can I select the suitable CNN architecture (VGGNet or GoogleNet ...), is there a preferable CNN architecture?
What do you think is the best library to do this work, how can I select the suitable library?
Thanks..
You can use tensorflow-slim. They have a library of many top pre-trained CNN models that you can use directly or fine-tune easily on your dataset. I think the training time depends on your hardware and amount of data you have.
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I am using the OpenCv Library for the object detection.
I downloaded the given example of face detection. In this example they are using haarcascade_frontalface_default.xml.
I have to detect Nail in hand. Please suggest me how can I do it?
I also saw this-
http://docs.opencv.org/doc/tutorials/features2d/detection_of_planar_objects/detection_of_planar_objects.html#detectionofplanarobjects
Please suggest me how I detect nail in hand.