| Keyword | CPC | PCC | Volume | Score | Length of keyword |
|---|---|---|---|---|---|
| tube tops intimates | 0.85 | 0.8 | 4635 | 55 | 19 |
| tube | 0.83 | 0.9 | 4876 | 50 | 4 |
| tops | 0.57 | 0.7 | 1997 | 16 | 4 |
| intimates | 1.46 | 0.4 | 3602 | 91 | 9 |
| Keyword | CPC | PCC | Volume | Score |
|---|---|---|---|---|
| tube tops intimates | 1.66 | 0.8 | 9448 | 81 |
https://towardsdatascience.com/dog-breed-classification-using-deep-learning-concepts-23213d67936c
Step 0: Import Datasets Step 0: Import DatasetsObviously, to be able to build an algorithm intended to identify dogs we will need some “dog data”. A lot of it. Thankfully, for this project Udacity is providing a decent number of dog images including the corresponding breed labels. Concretely, the image data comprises 8351 dog images and 133 separate dog breed names.Since the app has the additional task to assign the most resembling dog breed to a given human face, we also need a dataset with human faces. The dataset provided by Udacity includes 13233 images from the .Step 1: Detect Humans Step 1: Detect HumansThis seems to be a somewhat surprising step in the development of a dog identification app, but it is necessary for its extra job to assign the most resembling dog breed to a given human face.In order to detect human faces in images we will use OpenCV’s implementation of . The approach of this classifier is based on the concept of , which is widely used in the field of object recognition because of its convincing calculation speed.After instantiating a new pre-trained classifier, an image is loaded and converted to grayscale. Applying the classifier to the image gives us the bounding boxes of detected human faces.code for detection of human faces using OpenCV’s CascadeClassifierHere are a few examples from the dataset used in this project after running them through our cascade classifier:image source: Labeled faces in the wild , Assessing the human detectorLet’s now take a look at how the classifier performs with the pictures from our datasets. We apply the algorithm to 100 of our dog images and are curious to explore the 12 pictures in which the classifier interestingly identified human content. We are slightly disappointed to hardly find any bizarre and uncanny human features in the depicted dogs’ faces that could have fooled our algorithmimage source: dog image dataset provided by UdacityConversely, the classifier missed human faces in the following 2 of the 100 samples of human pictures used in our assessment:image source: Labeled faces in the wild , Still, our classifier seems reliable enough to give it a go in our project.Step 2: Detect Dogs Step 2: Detect DogsNow that we have a pretty decent algorithm to detect human faces in images we surely want to build a similar function for dog detection. Unfortunately, at the moment there is no comparable “dog detector” available for OpenCV’s CascadeClassifiers. Therefore, we choose another approach by employing an image classification model which has been pre-trained on the vast image database of . More specifically, we will use the high-level deep learning API to load the ResNet-50 convolutional neural network and run images through this model. For a specific image the network predicts probabilites for each of 1000 image categories in total. We attribute a positive dog detection to an image, if the model assigns the maximum probability to one of the 118 dog related categories.The source code below lists the functions used to preprocess the image data and run them through ResNet-50 model.Assessing the dog detectorHow does the ResNet-50 dog detector perform on our image datasets? We are going to test this with the source code below.We obtain a compelling 100% accuracy with our dog images, but Frank Solich might be worried that the only dog, that one of the most groundbreaking deep learning network models spotted in a human image dataset, was in a portrait of him:Frank Solich, image source: Labeled faces in the wild ,Step 3: Create a CNN to Classify Dog Breeds (from Scratch) Step 3: Create a CNN to Classify Dog Breeds (from Scratch)Now we will come to the really interesting part and tackle the implementation of the app’s principal task to tell the correct dog breed label from an image of a dog. We could make things easy and just use the pre-trained model from step two and predict the dog breed labels defined in the categories of the ImageNet dataset. But of course it’s much more exciting, interesting and educational to build our own solution, so here we go! Before we start building our own classifier, a few words about convolutional neural networks.Convolutional neural networks (CNNs) are a class of deep neural networks primarily used in the analysis of images. To a certain extent, the design of convolution networks was inspired by the way in which a mammal’s brain processes visual impressions. Translation invariance and shared weights are mostly cited to explain the advantages of CNNs over using other types of neural networks in image analysis. The architecture of a convolution network involves the use of multiple hidden layers that perform mathematical convolution operations on their input.First tryA typical example of the structure of a CNN is provided by Udacity, which suggests the following model for use in the present step.So we have an input layer into which image data are fed and a total of three pairs of convolutional and pooling layers before a fully connected “dense” layer produces an output. Convolutional layers consist of a set of filters of a certain height and width, while the pooling layers have the task of reducing the dimension of the input. Typically, the number of filters in each convolutional layer increases while the dimensionality of the processed data decreases. Because the performance of a model typically increases with increasing depth, we add two additional stages to the model proposed by Udacity.The source code for creating our model with the Keras library looks like this:Before producing an output we insert an additional dropout layer which randomly deactivates some of the neurons. The use of dropout layers is a common regularisation method to prevent overfitting of the training data.Finally, our first model looks like in the following graph:CNN from scratch (plotted with )Let’s now train our model by running our training set through our network 30 times:Let’s take a look at the progress our training made in each epoch:We can see an almost linear increase in accuracy, and by the end of the training we obtain roughly 23 % with our training set with rather moderate overfitting effects, meaning the accuracy for our validation set is clearly lagging behind but not that much. Our additional test with the test data gives an accuracy of 16.5 %.Not bad, but certainly not accurate enough for a serious application, so let’s see if we can do any better.Second try with AlexNetNow we’re going to try again with a true classic in the world of CNN models. is a CNN model that outclassed its competitors in the 2012 ImageNet Large Scale Visual Recognition Challenge and introduced some groundbreaking new concepts, such as the ReLU activation function and the use of dropout layers to prevent overfitting. The general structure of the model is as follows:AlexNet (plotted with )Let’s implement an AlexNet model with the Keras library:As in the first attempt, we run our training data 30 times through our network and achieve the following progress.So, wow, the accuracy of the training set is literally shooting upwards toward the end beating the results from our first attempt, but, oh dear, what is happening with the validation curve??? We are clearly dealing with an overfitting problem.3rd try. Tackling overfitting with data augmentationIn addition to using dropout layers, we can use another popular method to get our problem under control. Data augmentation is a a technique to increase the diversity of the training set by applying random transformations such as image rotation, image shifting, varying image brightness and image flipping. So let’s try this method in our next attempt:Ok, and now let’s check the progress history:Yep, that looks much better now, even if avoiding the overfitting issue was clearly at the expense of the accuracy levels, so that we only achieve 10.9 % with our test dataset. But if we compare the trajectory of the two graphs from our first and third attempt, AlexNet seems to have a steeper curve and could soon overtake the model from our first try in additional epochs.But all in all, the approach using a CNN model, that we build from scratch, seems to be quite complex, tedious and time-consuming, which requires a lot of patience and a lot of computing power. So, let’s look at a better method in the next step.Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning) Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning)The general idea behind transfer learning is the fact that it is much easier to teach specialized skills to a subject that already has basic knowledge in the specific domain. There are a lot of neural network models out there that already specialize in image recognition and have been trained on a huge amount of data. Our strategy now is to take advantage of such pre-trained networks and our plan can be outlined as follows:find a network model pre-trained for a general image classification taskload the model with the pre-trained weightsdrop the “top of the model”, i. e. the section with the fully connected layers, because the specific task of a model is generally defined by this part of the networkrun the new data through the convolutional part of the pre-trained model. (this is also called feature extraction and the output of this step is also called bottleneck features.)create a new network to define the specific task at hand and train it with the output (the bottleneck features) of the previous step.As we will see in a moment, the structure of the model into which we stuff the bottleneck features can usually be quite simple because a large part of the training work has already been done by the pre-trained model. In step 4 of this project Udacity is providing some kind of blueprint for this strategy by having already fed our images dataset into a pre-trained VGG16 model (another classic in the field of CNN models for image classification) and making available the output as bottleneck features, which we can now feed into a very simple training network that essentially consists of just one global average pooling layer and a final dense output layer.The following source code loads the bottleneck features, defines the top layers for our specific classification task, and trains these new layers with the bottleneck features:Again, let’s take a look at the progress history:Apart from the rapid training speed we observe a remarkable performance in terms of accuracy and achieve about 75 % with our test data, albeit at the expense of an obvious overfitting problem.Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning) Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)We will now take step 4 as a template and define our own CNN using transfer learning. We choose as the network that should provide us with the features for our training layers. Inception is another high performing model on the ImageNet dataset and its power lies in the fact that the network could be designed much deeper than other models by introducing subnetworks called inception modules.The source code looks quite similar to the one from step 4:To prevent the overfitting issues we observed in step 4, we inserted an additional dropout layer and added batch normalization before the output layer.As we can see from the progress history, we still have some overfitting problems, but we also notice another improvement of accuracy. We achieve 83 % accuracy with our test data.To make our model even better, we could consider the following options:using data augmentation to prevent overfittingadding layers to our simple training modelgetting more training dataBut for now, we are quite satisfied with the results from our latest attempt and use them in the algorithm we are going to write and test in the following steps.Step 6: Write your Algorithm Step 6: Write your AlgorithmSo, let’s now collect the achievements and findings from the previous steps and write an algorithm that takes an image of a dog or a human und spits out a dog breed along with 4 sample images of the specific breed.Step 7: Test your Algorithm Step 7: Test your AlgorithmFinally, let’s test our algorithm with a few test images.Doberman Pinscher, image source: dog image dataset provided by UdacityCollie, image source: dog image dataset provided by Udacityimage source: dog image dataset provided by UdacityConclusionIn this project we developed several approaches for the development of an app for the identification of dog breeds, and we achieved our best results with the application of a transfer learning model. We obtained an accuracy of 83% in our tests. We also learned how to build convolution networks from scratch, which was a very educational undertaking, even though we soon realized that there are significantly more promising methods, particularly with the application of transfer learning.However, we still see several options to further improve our algorithm in the future:We could gather more training data.We could employ data augmentation to prevent overfitting.We could add more layers to make our model more complex and hopefully more powerful.We could extend our training time and add more epochs to the training.But all in all, the accuracy levels from our tests, along with the tests with specific sample images, suggest that we already have a serious model we could work with in a real app.The source code for this project was written in Python in a Jupyter Notebook and is making use of the popular deep learning libraries and . You can find it in the corresponding .Some of the source code has been provided by Udacity in this .
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https://towardsdatascience.com/fast-ai-season-1-episode-2-2-dog-breed-classification-5555c0337d60
Sep 16, 2018 . Welcome to the 2nd part of Episode 2 where we will take on Dog Breed Classification problem. We have 120 breeds of dog images which we … login
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DA: 9 PA: 16 MOZ Rank: 96
https://aicamp.medium.com/build-dog-breads-classifier-step-by-step-with-aws-sagemaker-be85b25b32ff
May 31, 2020 . The app would need a classifier to predict dog breed from an image, and the best models for that are Deep Learning neural networks like ResNet. But DL models are large and processing intensive, so you should host your dog breed classifier in the cloud where the mobile app can access it via an API. login
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https://medium.com/nanonets/how-to-easily-build-a-dog-breed-image-classification-model-2fd214419cde
Mar 15, 2019 . Dog Breed Dataset. The dataset that we’ll be working on can be accessed here. We are provided a training set and a test set of images of dogs. Each image has a filename that is its unique id ... login
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DA: 75 PA: 35 MOZ Rank: 48
https://medium.com/@paul.stancliffe/udacity-dog-breed-classifier-project-walkthrough-e03c1baf5501
Mar 27, 2019 . output from dog breed classifier Overview. The project was part of Udacity’s Data Scientist nanodegree and is one of the most popular … login
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https://www.youtube.com/watch?v=JnskIHjAxkc
Nov 08, 2020 . Kaggle : https://www.kaggle.com/deepakat002/inception-xception-nasnetlarge-inceptionresYOU NEED TO RUN THIS NOTEBOOK ON KAGGLE This is a tutorial on how to u...
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https://www.bing.com/visualsearch/microsoft/whatdog
Identify a dog's breed, learn about its temperament, find similar dogs, and more. Upload a photo or take a picture with your phone to try it. The photos you provided may be used to improve Bing image processing services. login
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https://www.kdnuggets.com/2016/02/what-dog-breed-ai-fetch.html
Wish granted! Fetching the dog breed information has never been easier. Microsoft Garage recently launched an app called “Fetch!” that can identify a dog’s breed from pictures. The app uses Artificial Intelligence (AI) for classifying the image of a dog to its knowledge base of over a 100 popular pure breeds. login
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https://www.revivalanimal.com/pet-health/artificial-insemination/learning-center
Artificial Insemination (AI) for Dogs How to Tell if a Dog is in Heat When a female dog begins her heat cycle (proestrus), her vulva will swell and she will bleed for three to nine days. During this time, the uterus is growing a new lining for the embryo. When her bleeding subsides, she will be receptive to the stud and stand to breed (estrus).
DA: 74 PA: 38 MOZ Rank: 12
https://processing-with-ai.gitlab.io/part2/transfer-learning/
Now, let's say I want to classify a dog of an unknown breed. Mr. Youpee is 27cm high and weight 12.6kgs. A KNN algorithm would look at the Euclidean Distance between our new example and our previous data to determine which ones are its closest neighbors. Then, by looking at the category they belong to, it can predict which breed our new dog is. login
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https://github.com/topics/dog-breed-classifier
Sep 16, 2021 . Dog Breed classifier project of the Data Scientist Nanodegree by Udacity. A Web Application is developed using Flask through which a user can check if an uploaded image is that of a dog or human. Also, if the uploaded image is that of a human, the algorithm tells the user what dog breed the human resembles the most. login
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DA: 53 PA: 62 MOZ Rank: 17
https://www.kaggle.com/c/dog-breed-identification
Dog Breed Identification | Kaggle. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. By using Kaggle, you agree to our use of cookies. Got it. Learn more. login
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https://devmesh.intel.com/projects/dog-breed-classifier
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https://github.com/hmelendez001/Capstone-Project4-Udacity-Data-Scientist
As part of the udacity.com Data Scientist nanodegree, this is project four and the final project of the program. This project involves Convolutional Neural Networks, including a web application that allows the user to upload an image of a dog or a human, and I will showcase an AI that will output the closest dog breed resemblance to the image.
DA: 3 PA: 29 MOZ Rank: 1
https://www.slideshare.net/angjoo/dog-breed-classification-using-part-localization
Feb 12, 2013 . After humans, dogs are possibly the most photographed species (perhaps after cats) on the internet.Determination of dog breeds is a very challenging task, sharing many of the challenges seen in fine-grained classification, and success in this domain will certainly lead to further success in broader domain of automatic species identification ...
DA: 35 PA: 53 MOZ Rank: 90
https://www.analyticsvidhya.com/blog/2021/06/beginner-friendly-project-cat-and-dog-classification-using-cnn/
Jun 01, 2021 . The Dogs vs Cats dataset can be downloaded from the Kaggle website. The dataset contains a set of images of cats and dogs. Our main aim here is for the model to learn various distinctive features of cat and dog. Once the training of the model is done it will be able to differentiate images of cat and dog. Installing Required Packages for Python ... login
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http://machinememos.com/python/keras/artificial%20intelligence/machine%20learning/transfer%20learning/dog%20breed/neural%20networks/convolutional%20neural%20network/tensorflow/image%20classification/imagenet/2017/07/11/dog-breed-image-classification.html
Jul 11, 2017 . Dog Breed Classification with Keras. Recently, I got my hands on a very interesting dataset that is part of the Udacity AI Nanodegree. In several of my previous posts I discussed the enormous potential of transfer learning. As a matter of fact, very few people train an entire convolutional network from scratch (with random initialization ... login
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DA: 94 PA: 96 MOZ Rank: 76
https://www.researchgate.net/publication/5654967_Classification_of_dog_barks_A_machine_learning_approach
dogs were easily recognized, as with d23 (733 recordings, kapp a = 75%) or d2 4 (1 ,524 rec ord ings , kap pa = 69% ), others we re very poorly recog nized, possi bly because o f the login
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DA: 69 PA: 83 MOZ Rank: 70
https://aaronlws.medium.com/dog-breed-classification-app-udacity-dsnd-1a117cd90af9
Project OverviewIn this project, we develop an algorithm that takes in an image and identifies if contains a dog or a human. If it does contain either a dog or a human, the algorithm will classify the dog’s breed or the dog breed that closely resembles that human. This problem falls under the popular category of … Problem StatementGiven the project, we would need an algorithm that solves 3 main problems: 1. Identify if the image contains human(s) 2. Identify if the image contains dog(s) 3. Classify the image to a specific breed of dog To solve these problems we will be using machine learning methods whic… DatasetsFor the dog breed classifier, we will use the Stanford Dogs Dataset. We use a versionthat contains about 8351 images with 133 different breeds to classify. We will use this dataset for training, validation, and testing. When training our model, we need to keep in mind the class dist… login
Project OverviewIn this project, we develop an algorithm that takes in an image and identifies if contains a dog or a human. If it does contain either a dog or a human, the algorithm will classify the dog’s breed or the dog breed that closely resembles that human. This problem falls under the popular category of …
Problem StatementGiven the project, we would need an algorithm that solves 3 main problems: 1. Identify if the image contains human(s) 2. Identify if the image contains dog(s) 3. Classify the image to a specific breed of dog To solve these problems we will be using machine learning methods whic…
DatasetsFor the dog breed classifier, we will use the Stanford Dogs Dataset. We use a versionthat contains about 8351 images with 133 different breeds to classify. We will use this dataset for training, validation, and testing. When training our model, we need to keep in mind the class dist…
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DA: 75 PA: 34 MOZ Rank: 15
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