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Pytorch cnn image classification

Models in PyTorch. Aug 10, 2018 MNIST contains 70,000 images of handwritten digits: 60,000 for PyTorch is a very popular framework for deep learning like . By contrast, most of the implementations involve learning individual image representations disjunctive with the previous frames in the video. If you have a fairly large set of 'good' and 'bad' images, You can use a convolutional neural network (CNN) with a package like pytorch or tensorflow (pytorch is more pythonic). I am classifying images and currently getting 65% accuracy with simple model how should I enhance it to achieve maximum accuracy. Define a loss function 4. Neural networks have been at the forefront of Artificial Intelligence research during the last few years, and have provided solutions to many difficult problems like image classification, language translation or Alpha Go. This course covers the parts of building enterprise-grade image classification systems like image pre-processing, picking between CNNs and DNNs, calculating output dimensions of CNNs, and leveraging pre-trained models using PyTorch transfer learning. (Pytorch) Use Case 2: Object Image Classification. What makes this problem difficult is that the sequences can vary in length, be comprised of a very large vocabulary of input Recently, I started up with an NLP competition on Kaggle called Quora Question insincerity challenge. Sometimes it’s worth to monitor how good or bad the model is training in real-time. It reviews the fundamental concepts of convolution and image analysis; shows you how to create a simple convolutional neural network (CNN) with PyTorch; and demonstrates how using transfer learning with a deep CNN to train on image datasets can generate state-of-the The CNN in PyTorch is defined in the following way: torch. One of the variables needed for gradient computation has been modified by an inplace operation,customize loss function Transfer Learning is expected to be the next driver of Machine Learning commercial success in Image Classification. LeNet(1998), AlexNet(2012), VGGNet(2014), GoogleNet(2014), ResNet(2015) are some of the famous CNN architectures in use now. MLP MNIST - basic introduction to torchvision; CNN (LeNet-5) http://vision. Think of convolution as applying a filter to our image. 07146) DenseNet (1608. 06993) Let’s use a Classification Cross-Entropy loss and SGD with momentum. In the first part of this post, we’ll discuss the OpenCV 3. Let’s take things up a notch. GitHub Gist: instantly share code, notes, and snippets. Among them, PyTorch from Facebook AI Research is very unique  What about data? Generally, when you have to deal with image, text, audio or video data, you can use standard python packages that load data into a numpy  Nov 20, 2018 How to Train an Image Classifier in PyTorch and use it to Perform Basic And if you use a cloud VM for your deep learning development and  Nov 6, 2018 I recently finished work on a CNN image classification using PyTorch library. You can see how this was done in the image above. Learn the role of the rest of the layers in a CNN, choosing the right loss function and optimizer to detect multiple classes PyTorch is an open source deep learning platform with a rich ecosystem that enables seamless integration from research prototyping to production deployment. Let’s get ready to learn about neural network programming and PyTorch! In this video, we will look at the prerequisites needed to be best prepared. It also includes a use-case in which we will create an image How to decide number of layers and final model in CNN to increase the accuracy of the prediction. It reviews the fundamental concepts of convolution and image analysis; shows you how to create a simple convolutional neural network (CNN) with PyTorch; and demonstrates how using transfer learning with a deep CNN to train on image datasets can generate state-of-the-art performance. In the tutorial on artificial neural network, you had an accuracy of 96%, which is lower the CNN. Using AWS SageMaker, we can quickly build, train and deploy machine learning and deep learning models in a production-ready serverless hosted environment. In my mind, there are five basic types of problems that are well-suited for neural networks: multiclass classification, binary classification, regression, CNN image classification, and LSTM sentiment analysis. As per wikipedia, “PyTorch is an open source machine learning  IN PROGRESS. A typical CNN process in TensorFlow looks like this: Following is a typical process to perform TensorFlow image classification: Pre-process data to generate the input of the neural network – to learn more see our guide on Using Neural Networks for Image Recognition. Code. Prerequisite: Image Classifier using CNN. The subsequent posts each cover a case of fetching data- one for image data and another for text data. In such cases, the interwoven patterns in an optical flow are expected to hold higher significance. Jun 3, 2019 Image Classification using Transfer Learning in PyTorch The word pre-trained here means that the deep learning architectures AlexNet and  4 days ago A GitHub repo Benchmark on Deep Learning Frameworks and GPUs reported that PyTorch is faster than the other framework in term of images  Jan 28, 2019 Because this PyTorch image classifier was built as a final project for a . The Fashion-MNST dataset contains Zalando’s article images with 60,000 images in the training set and 10,000 in the test set. Then you can convert this array into a torch. We pass over a mini image, usually called a kernel, and output the resulting, filtered subset of our image. As per wikipedia, “PyTorch is an open source machine learning library for Python, based on Torch, used for PyTorch Image Classification. I recently finished work on a CNN image classification using PyTorch library. That’s it! All it takes is these 4 steps to carry out image classification using pre-trained models. That said, Keras, being much simpler than PyTorch, is by no means a toy – it’s a serious deep learning tool used by beginners, and seasoned data scientists alike. Deploying and serving CNN based PyTorch models in production has become simple, seamless and scalable through AWS SageMaker. Introduction to pyTorch #1 : The stochastic gradient algorithm; Introduction to pyTorch #2 : The linear regression; The FashionMNIST dataset. Learn how to set up an input convolutional layer and output layer. . The farmer just has to take an image of the crop and the image will be uploaded to the server. I published my code on GitHub. Implementing a CNN in PyTorch is pretty simple given that they provide a base class for all popular and commonly used neural network modules called torch. Feb 28, 2019 In this post we'll classify an image with PyTorch. Computation time and cost are critical resources in building deep models, yet many existing benchmarks focus solely on model accuracy. We will use the popular MNIST  Nov 3, 2017 Extract a feature vector for any image with PyTorch input image, and can be used for a variety of tasks such as ranking, classification, or clustering. Mar 9, 2019 Convolution Idea: While for an image we move our conv filter horizontally as well as Here is the text classification network coded in Pytorch:. I am using Fashion MNIST dataset. resnet101 has about 44. The CNN neural network has performed far better than ANN or logistic regression. 4. Image classification is a method to classify the images into their respective category classes using some method like : Training a small network from scratch; Fine tuning the top layers of the model using VGG16; Let’s discuss how to train model from scratch and classify the data containing cars and By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease. But while it seems that literally everyone is using a neural network today, creating and training your own neural network for the first time can be quite a hurdle to overcome. Simple Image Classification using Convolutional Neural Network — Deep Learning in python. The CNN gets its name from the process of Convolution, which is the first filter applied as part of the feature-engineering step. As usual, we've 60k training images and 10k testing images. You'll get the lates papers with code and state-of-the-art methods. Train the network on the training data 5. It provides a simple implementation of the CNN algorithm using the framework PyTorch on Python. This Edureka PyTorch Tutorial video (Blog: https://goo. It is common practice in deep learning to start with a model that has already  Jan 6, 2019 Quick start for PyTorch by showing how to adjust ResNet architecture to create deep learning image classifier for MNIST dataset with 99%  Dec 18, 2017 transfer learning; finetuning; image classification; deep learning In this post we used PyTorch to perform transfer learning in different datasets  May 1, 2018 Introduction to CNN and PyTorch - Kripasindhu Sarkar - May 2018. Processing. Multi-class Image classification using CNN over PyTorch, and the basics of CNN (self. Fast R-CNN replaced the SVM classifier with a softmax layer on top of the CNN to output a classification. Summary DAWNBench is a benchmark suite for end-to-end deep learning training and inference. CVPR 2016), which has been included by ModelDepot. nn. Understanding PyTorch’s In this post, we describe how to do image classification in PyTorch. Deep Learning with OpenCV. This dataset is also included in the torchvision package. You will This video teaches you how to build a powerful image classifier in just minutes using convolutional neural networks and PyTorch. There are many free courses that can be found on the internet. Jul 30, 2019 Introduction to Image Classification using Pytorch to Classify scientist and deep learning expert Ian Goodfellow calls for people to move away  Arabic name classification with Scikit-Learn and Pytorch Posted on Sat 01 June 2019 in Machine Learning • Tagged with python, pytorch, cnn, torchvision, mnist, skl CIFAR-10 is a classic image recognition problem, consisting of 60,000  May 3, 2019 In this episode, we will pass a batch of images to our convolutional neural network (CNN) and interpret the results. Image Style Transfer Using Convolutional Neural Networks by Leon A. 5 million parameters tuned during the training process. In this tutorial, we will present a few simple yet effective methods that you can use to build a powerful image classifier, using only very few training examples --just a few hundred or thousand pictures from each class you want to be able to recognize. If you have ever wondered, why bother with Pytorch when there are several other frameworks out there, then this is for you. PyTorch sells itself on three different features: A simple, easy-to-use interface Research teams have introduced several network architectures for solving the problem of image classification and related computer vision tasks. How about we try the same with ResNet? 1. PyTorch 是一个 Python 优先的深度学习框架,能够在强大的 GPU 加速基础上实现张量和动态神经网络。本站提供最新以及最全面的 PyTorch 中文新闻,教程及文档。 本站微信群、QQ群: QQ一群 (242251466) QQ二群 (785403617) [新建] Rogier van der Geer shows how you can use PyTorch to build out a Convolutional Neural Network for image classification: The tool that we are going to use to make a classifier is called a convolutional neural network, or CNN. My goal is to show you how you can use deep learning and computer vision to assist radiologists in automatically diagnosing severe knee injuries from MRI scans. This article will explain the Convolutional Neural Network (CNN) with an illustration of image classification. Olah). Module class. CVPR 2019 • rwightman/pytorch-image-models • Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. Image classification in PyTorch. , a deep learning model that can recognize if Santa Claus is in an image or not): This video teaches you how to build a powerful image classifier in just minutes using convolutional neural networks and PyTorch. The performances of the CNN are impressive with a larger image set, both in term of speed computation and accuracy. We will take an image as input, and predict its description using a Deep Learning model. CV  Welcome to deep learning for image classification tutorial! Learn the basics of PyTorch, a powerful but easy to use package for scientific computing (and deep  Dec 9, 2018 This Blog post is about building a Deep learning model which does the image classification using Pytorch. We also provide Torch implementation and MXNet implementation. It provides easy to use building blocks for training deep learning models. In fact, it is only numbers that machines see in an image. In the initialization part of the class, you should collect a list of all the images and its labels in the dataset. Image classification is done with the help of a pre-trained model. It is an NLP Challenge on text classification, and as the problem has become more clear after working through the competition as well as by going through the invaluable kernels put up by the kaggle experts, I thought of sharing the knowledge. What Is Image Classification. We’ll get an overview of the series, and we’ll get a sneak peek at a project we’ll be working on. class ( or classes) of an image using a trained deep learning model. 05027) WRN (1605. CNNs are primarily used for computer vision, powering tasks like image classification, face recognition, identifying and classifying everyday objects, and image processing in robots and autonomous vehicles. How to quickly use PyTorch components to build classifier. That’s huge! PyTorch Image Classification. Convolutional Neural Networks (CNN), a technique within the broader Deep Learning field, have been a revolutionary force in Computer Vision applications, especially in the past half-decade or so. The AI model will be able to learn  Pytorch Image Recognition with PyTorch Introduction, What is PyTorch, such as max pooling, padding and stride configuration, CNN filters work on images to   Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ) PyTorch. Understanding intuitively how CNNs work. The code for this example can be found on GitHub. Personally, I suggest the course of Andrej Karpathy at Stanford. It’ll help to understand, debug and optimize your models without waiting till the model get trained to monitor the performance. Methodology / Approach. Personally, I suggest the course of Andrej Karpathy (@karpathy) at Stanford. 3 release and the overhauled dnn module. Chief of all PyTorch’s features is its define-by-run approach that makes it possible to change the structure of neural networks on the fly, unlike other deep learning libraries that rely on inflexible static graphs. This was perhaps the first semi-supervised approach for semantic segmentation using fully convolutional networks. 1) Pre-trained model. This video teaches you how to build a powerful image classifier in just minutes using convolutional neural networks and PyTorch. Let’s look at a simple implementation of image captioning in Pytorch. Source: Stanford Deep Learning 2 days ago · In this course, Image Classification with PyTorch, you will gain the ability to design and implement image classifications using PyTorch, which is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease-of-use and built-in support for optimized hardware such as GPUs. Set up the deep learning environment using the PyTorch library Learn to build a deep learning model for image classification PyTorch Use Case: Training an Image Classifier. Introduction¶. Bag of Tricks for Image Classification with Convolutional Neural Networks. Test the network on the testing data import torch from torch. pdf - introduction to  May 1, 2018 In this post, I want to introduce one of the popular Deep Learning frameworks, PyTorch, by implementing a simple example of a Convolutional  PyTorch is a powerful deep learning framework which is rising in popularity, and it to state-of-the-art results in image classification it is necessary to go deeper. Nov 29, 2017 In the image below is the network we will build in this part, which we can use to classify hand written digits. ), compare the differences between those implementations(in most cases, they are the same), for the different part, I would go to the paper for more details, if the paper didn't have these details, I would choose the popular framwork's implementation. Some sailent features of this approach are: Decouples the classification and the segmentation tasks, thus enabling pre-trained classification networks to be plugged and played. This categorized data may then be used to produce thematic maps of the land cover present in an image. Neural networks are everywhere nowadays. As PyTorch's documentation on transfer learning explains, there are two major ways that transfer learning is used: fine-tuning a CNN or by using the CNN as a fixed feature extractor. e. it sits on top of Pytorch, which is a very In this post, I’ll describe in detail how R-CNN (Regions with CNN features), a recently introduced deep learning based object detection and classification method works. learnpython) submitted 12 minutes ago by thevatsalsaglani I always had this conundrum about how an image goes through a Convolutional Neural Network; in this blog, I try to address it and build a multi-class classifier for classifying apparels from scratch We’re going to try to create a deep learning CNN model based on data from or provide image classification files. You can adapt this problem statement to any type of image classification that interests you. Feb 25, 2019 AI & Deep Learning enthusiast | Student at Minerva. AlexNet implementation is very easy after the releasing of so many deep learning libraries. As I understand it, nowadays for image tasks, CNNs are what all the cool kids use. If you are interested in learning an impactful medical application of artificial intelligence, this series of articles is the one you should looking at. Reutilizing deep networks is impacting both research and industry. g. In my article, I show how to use Keras to create a prediction model for the well-known MNIST image data set. If you prefer to PyTorch is one of the newer members of the deep learning framework family. Module (refer to the official stable documentation here). What is Image Classification? The intent of Image Classification is to categorize all pixels in a digital image into one of several land cover classes or themes. Define a CNN 3. PyTorch takes advantage of the power of Graphical Processing Units (GPUs) to make implementing a deep neural network faster than training a network on a CPU. We will be building a convolutional neural network that will be trained on few thousand images of cats and dogs, and later be able to predict if the given image is of a cat or a dog. So in this tutorial, we will show how it is possible to obtain very good image classification performance with a pre-trained deep neural network that will be used to extract relevant features and a linear SVM that will be trained on these features to classify the images. We will go over the dataset preparation, data augmentation and then steps to build the classifier. CNN scans an image one area at a time, identifies and extracts important features, and uses them to classify the image. A place to discuss PyTorch code, issues, install, research. Ecker, and Matthias Bethge. determining whether a picture is that of a dog or cat. Using ResNet for Image Classification. The goal is to minimize or remove the need for human intervention. ResNet (1512. A similar situation arises in image classification, where manually engineered features (obtained by applying a number of filters) could be used in classification algorithms. In this course, Image Classification with PyTorch, you will gain the ability to design and implement image classifications using PyTorch, which is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease-of-use and built-in support for optimized hardware such as GPUs. However, with the advent of deep learning, it has been shown that convolutional neural networks (CNN) can outperform this strategy. Following papers are implemented using PyTorch. The good old method of printing out training losses… This repo provides PyTorch Implementation of MSG-Net (ours) and Neural Style (Gatys et al. R-CNN’s have proved highly effective in detecting and classifying objects in natural images, achieving mAP scores far higher than previous techniques. This is a step-by-step guide to build an image classifier. You can find a great explanation of what these are right here on wikipedia. • Learn how to load a pre trained CNN network • Learn how to prepare images for classification with CNN network • Learn how to do classification using prepared images and CNN You'll also see how you can leverage the power of transfer learning by using pre-trained models for image classification. To learn how to use PyTorch, begin with our Getting Started Tutorials. In this post, we explain what is Transfer Learning and when to use its different strategies. Image classification is a supervised learning problem. 03385) ResNet-preact (1603. Conv2D(Depth_of_input_image, Depth_of_filter, size_of_filter, padding, strides) Depth of the input image is generally 3 for RGB, and 1 for Grayscale. The course uses fastai, a deep learning library built on top of PyTorch. In this post, we’ll cover how to write a simple model in PyTorch, compute the loss and define an optimizer. PyTorch is grabbing the attention of deep learning researchers and data science practitioners due to its simplicity of use, accessibility, efficiency, and being more native to Python way of What is Image Classification in Remote Sensing? Image classification is the process of assigning land cover classes to pixels. In this use case, we will create convolutional neural network (CNN) architectures in PyTorch. In Tutorials. We’ll then write a Python script that will use OpenCV and GoogleLeNet (pre-trained on ImageNet) to classify images. PyTorch is one of the leading deep learning frameworks, being at the same time both powerful and easy to use. Multi-class Image classification with CNN using PyTorch Welcome to PyTorch Tutorials¶. autograd import This article will explain the Convolutional Neural Network (CNN) with an illustration of image classification. One main use-case is that of image classification, e. The model is defined in two steps. Machine learning - Classification. May 17, 2018 Many deep learning frameworks have been released over the past few years. We will use resnet101 – a 101 layer Convolutional Neural Network. Gatys, Alexander S. 1 day ago · In this course, Image Classification with PyTorch, you will gain the ability to design and implement image classifications using PyTorch, which is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease-of-use and built-in support for optimized hardware such as GPUs. PyTorch is a scientific computing package developed by Facebook for Deep Learning. How to build your first image classifier using PyTorch. For each paper I've implemented, I read other people's implements(not just pytorch implementations, include keras, tf, mxnet, etc. What you will learn. A model can be defined in PyTorch by subclassing the torch. Generally, when you have to deal with image, text, audio or video data, you can use standard python packages that load data into a Numpy array. First, to give some context, recall that LSTM are used as Recurrent Neural Networks (RNN). For real-world video classification use cases it is imperative to capture the spatiotemporal features. Pre-trained models are neural network models which are trained on large benchmark datasets like ImageNet. Image Classification is a task of assigning a class label to the input image from a list of given class labels. It reviews the fundamental concepts of convolution and image analysis; shows you how to create a simple convolutional neural network (CNN) with PyTorch; and demonstrates how using transfer learning with a deep CNN to train on image datasets can generate state-of-the I know there are many blogs about CNN and multi-class classification, but maybe this blog wouldn’t be that similar to the other blogs. Given an image predict the label . We will then train the CNN on the CIFAR-10 data set to be able to classify images from the CIFAR-10 testing set into the ten categories present in the data set. As per wikipedia, “PyTorch is an open source machine learning library for Python, based on Torch, used for applications such as natural language processing. The 60-minute blitz is the most common starting point, and provides a broad view into how to use PyTorch from the basics all the way into constructing deep neural networks. Tip: you can also follow us on Twitter The CNN model will be trained using different crop disease images and will be able to classify the disease type. You will learn the basics of building a CNN for image classification. Next, you’ll move on to image classification using convolutional neural networks; you’ll study the role of convolutional and pooling layers and the basic structure of a CNN, you’ll then build a CNN to classify images from the Cifar-10 dataset. Get this from a library! Image analysis and text classification using CNNs in PyTorch : learn to build powerful image and document classifiers in minutes. Introduction to CNN and PyTorch - Kripasindhu Sarkar - May 2018 Machine learning - Classification Given an image predict the label Training and evaluating deep learning models may take a lot of time. When fine-tuning a CNN, you use the weights the pretrained network has instead of randomly initializing them, and then you train like normal. [Goku Mohandas; Alfredo Canziani] -- "This video teaches you how to build a powerful image classifier in just minutes using convolutional neural networks and PyTorch. Here the idea is that you are given an image and there could be several classes that the image belong to. Sequence classification is a predictive modeling problem where you have some sequence of inputs over space or time and the task is to predict a category for the sequence. The farmer will be notified about the disease and from here, one can do a further procedure to solve the disease. PyTorch has seen increasing popularity with deep learning researchers thanks to its speed and flexibility. AlexNet was the pioneer in CNN and open the whole new research era. Sun 05 June 2016 By Francois Chollet. I am classifying images and currently getting 65% accuracy with simple model how should I enhance i How to perform image classification using Deep Learning Convolutional Neural Networks PyTorch Lecture 10: Basic CNN A friendly introduction to Convolutional Neural Networks and Image Image Classification. It reviews the fundamental concepts of convolution and image analysis; shows you how to create a simple convolutional neural network (CNN) with PyTorch; and demonstrates how using transfer learning with a deep CNN to train on image datasets can generate state-of-the Working with PyTorch may offer you more food for thought regarding the core deep learning concepts, like backpropagation, and the rest of the training process. PyTorch is one of the most popular frameworks of Deep learning. Let us display an image from the test set to get familiar. *Tensor. I spent most of the time optimizing hyperparameters and tuning image augmentation. The solution builds an image classification system using a convolutional neural network with 50 hidden layers, pretrained on 350,000 images in an ImageNet dataset to generate visual features of the images by removing the last network layer. Image classification with Keras and deep learning. This will be a hands-on tutorial quickly getting to speed with image classification using PyTorch, starting with the autograd function, CNN fundamentals and ending with the benefits of transfer learning. If you are just getting started with Tensorflow, then it would be a good idea to read the basic Tensorflow tutorial here. It is a simple feed-forward convolutional neural network (CNN), which takes a 28 x 28 pixel, greyscale, input image, that is then fed through several layers, one after the other, and finally gives an output vector, which contain the log probability (since we will use the Negative Log Likelihood loss function) that the input was one of the Year after the publication of AlexNet was published, all the entries in ImageNet competition use the Convolutional Neural Network for the classification task. Each sample is a 28×20 grayscale image with a label from 10 classes. Unrolling recurrent neural network over time (credit: C. pytorch cnn image encoder. This blog post is part two in our three-part series of building a Not Santa deep learning classifier (i. gl/4zxMfU) will help you in understanding various important basics of PyTorch. The original author of this code is Yunjey Choi. We will perform object image classification using the popular CIFAR-10 dataset. Where earlier we had different models to extract image features (CNN), classify (SVM), and tighten bounding boxes (regressor), Fast R-CNN instead used a single network to compute all three. In this post, you’ll learn from scratch how to build a complete image classification pipeline with PyTorch. Subsequently, we'll PyTorch's LSTM module handles all the other weights for our other gates. We know that the machine’s perception of an image is completely different from what we see. RNNs are neural networks that used previous output as inputs. Finally, you'll get to work with recurrent neural networks for sequence data, seeing how the dynamic computation graph execution in PyTorch makes building RNNs very simple. Classifies an image as containing either a dog or a cat (using Kaggle's public dataset), but could easily be extended to other image classification problems. There are a few basic things about an Image Classification problem that you must know before you deep dive in building the convolutional neural network. edu/cs598_spring07/papers/Lecun98. Overview Of Convolutional Neural Network In Image Classification; Introductory Guide To PyTorch Using A Linear Regression Problem; Why PyTorch ? So why do we need to learn PyTorch too in a world that abounds with DL frameworks. For images, packages such as Pillow and OpenCV are useful. In this notebook we will use PyTorch to construct a convolutional neural network. Hats off to his excellent examples in Pytorch! How to decide number of layers and final model in CNN to increase the accuracy of the prediction. For example, these 9 global land cover data sets classify images into forest, urban, agriculture and other classes. In this Tensorflow tutorial, we shall build a convolutional neural network based image classifier using Tensorflow. We will use a subset of the CalTech256 dataset to classify images of 10 different kinds of animals. In order to achieve this, you have to implement at least two methods, __getitem__ and __len__ so that each training sample (in image classification, a sample means an image plus its class label) can be accessed by its index. stanford. For audio, packages such as Scipy and We will build an image classifier using the Convolutional Neural Network (CNN) model to predict if a given image is that of Drogon or Vicerion (any Game of Thrones fans here in the house? Clap to say yay!). pytorch cnn image classification

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