Love dogs? : , : , : , : , : , : : : Kingdom Animalia Phylum Chordata Class Mammalia Order Carnivora Family Canidae Genus Canis , Species C. lupus , Subspecies C. l. familiaris making potentially the first animal to be domesticated. In that time, dogs have played many roles and performed many for their human . Dogs come in a startling variety of shapes and sizes, but from the giant and noble Great Dane to the tiny and tenacious Chihuahua, they are all one species with one basic history. The relationship between people and dogs goes back at least 15,000 years, dogs jobs companions In this article, we’ll explore how we can use AI to classify different breeds of dogs, from tall to short, from cute to agressive the easiest way. We’re going to use Deep Learning Studio to classify different breeds of Dogs. If you’re unaware of Deep Learning Studio do check my medium article… _Kingdom:Plantae Clade:Angiosperms Order:Asparagales Family:Iridaceae Subfamily:Iridoideae Tribe:Irideae Genus:Iris_towardsdatascience.com Iris genus classification|DeepCognition| Azure ML studio Dataset We’re going to use Stanford Dogs dataset which contains images of 120 distinct breeds of dogs. The dataset contains 10222 overall images of dogs. You need to upload the dataset in your account after altering it according to Deep Learning Studio. Don’t worry, I have done it for you. Download the dataset from directly upload it to your deep cognition’s account. my github repo and Some images from our Dataset: Left: , Right: Afghan Hound Briard Let’s get started by uploading the dataset: Uploading the dataset Create New Project: •Create a new project and name it as ‘Dog breeds classification’. Create new project Choose the uploaded the dataset in ‘ tab Datasets’ Model Now, here comes the heart of this project. But with Deep Learning Studio, this is the easiest part. We’ll just use 4 layers to classify the breeds of Dogs and get upto 88% accuracy. Let’s see how! Left: , Right: Model Architecture Complete view We’ll use WideResNet to classify our dataset. You can just drag-drop the Pre-trained WideResNet model from the left pane and you’re done!! Did you see how simple is that!? Hyperparameters: For any Deep Learning model, hyperparameters plays a major role. For this problem we’ll Adam Optimizer with categorical_crossentropy as loss function. We are using categorical cross entropy as loss function because we have categorical output(any one out of 120 breeds) with 25 epochs. Hyperparameters Training Let’s start training the model: Press the ‘ button to start training. Deep Cognition provides you 2hrs of free usage of GPUs. Start any one of the GPU instance to train the model. GPUs help training the model faster. start training’ Left: Right: training loss, Training Accuracy Did you see how easy it is to create an AI using Deep Learning Studio. Want to deploy it in market?! No problem! Let’s go to DLS’s deploy tab to generate a WebApp automatically. Deploying our model for usage Let’s check our model with any input Let’s choose an photo from our dataset and see our model predicts correctly. Afghan Hound’s Model correctly predicts Afghan Hound with an accurcay of 99.99%. Yo! So our model works perfectly fine. We could still improve the accuracy of our model but this was for demonstration purpose. Try out yourself with different model architecture and hyperparameters. Thanks for reading! If you liked this article, do clap 👏 and follow me on LinkedIn and Medium. Thanks. _View Manik Soni's profile on LinkedIn, the world's largest professional community. Manik has 2 jobs listed on their…_www.linkedin.com Manik Soni - Machine Learning Researcher - Self-Employed | LinkedIn _Read writing from Manik Soni on Medium. Machine Learning Researcher. Every day, Manik Soni and thousands of other…_medium.com Manik Soni - Medium Happy Deep Learning.
