Building an image classifier using keras

Building an image classifier in TF2

This repository is about building an Image classifier CNN with Python on Plant Disease Prediction

Image Classifier Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smartphone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice, you'd train this classifier, then export it for use in your application. We'll be using this dataset of 102 flower categories. When you've completed this project, you'll have an application that can be trained on any set of labelled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. This is the final Project of the Udacity AI with Python Nanodegree Prerequisites The Code is written in Python 3.6.5 . If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install pip run in the command Line python -m ensurepip -- default-pip to upgrade it python -m pip install -- upgrade pip setuptools wheel to upgrade Python pip install python -- upgrade Additional Packages that are required are: Numpy, Pandas, MatplotLib, Pytorch, PIL and json. You can donwload them using pip pip install numpy pandas matplotlib pil or conda conda install numpy pandas matplotlib pil In order to intall Pytorch head over to the Pytorch site select your specs and follow the instructions given. Viewing the Jyputer Notebook In order to better view and work on the jupyter Notebook I encourage you to use nbviewer . You can simply copy and paste the link to this website and you will be able to edit it without any problem. Alternatively you can clone the repository using git clone https://github.com/fotisk07/Image-Classifier/ then in the command Line type, after you have downloaded jupyter notebook type jupyter notebook locate the notebook and run it. Command Line Application Train a new network on a data set with train.py Basic Usage : python train.py data_directory Prints out current epoch, training loss, validation loss, and validation accuracy as the netowrk trains Options: Set direcotry to save checkpoints: python train.py data_dor --save_dir save_directory Choose arcitecture (alexnet, densenet121 or vgg16 available): pytnon train.py data_dir --arch "vgg16" Set hyperparameters: python train.py data_dir --learning_rate 0.001 --hidden_layer1 120 --epochs 20 Use GPU for training: python train.py data_dir --gpu gpu Predict flower name from an image with predict.py along with the probability of that name. That is you'll pass in a single image /path/to/image and return the flower name and class probability Basic usage: python predict.py /path/to/image checkpoint Options: Return top K most likely classes: python predict.py input checkpoint ---top_k 3 Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_To_name.json Use GPU for inference: python predict.py input checkpoint --gpu Json file In order for the network to print out the name of the flower a .json file is required. If you aren't familiar with json you can find information here. By using a .json file the data can be sorted into folders with numbers and those numbers will correspond to specific names specified in the .json file. Data and the json file The data used specifically for this assignemnt are a flower database are not provided in the repository as it's larger than what github allows. Nevertheless, feel free to create your own databases and train the model on them to use with your own projects. The structure of your data should be the following: The data need to comprised of 3 folders, test, train and validate. Generally the proportions should be 70% training 10% validate and 20% test. Inside the train, test and validate folders there should be folders bearing a specific number which corresponds to a specific category, clarified in the json file. For example if we have the image a.jpj and it is a rose it could be in a path like this /test/5/a.jpg and json file would be like this {...5:"rose",...}. Make sure to include a lot of photos of your catagories (more than 10) with different angles and different lighting conditions in order for the network to generalize better. GPU As the network makes use of a sophisticated deep convolutional neural network the training process is impossible to be done by a common laptop. In order to train your models to your local machine you have three options Cuda -- If you have an NVIDIA GPU then you can install CUDA from here. With Cuda you will be able to train your model however the process will still be time consuming Cloud Services -- There are many paid cloud services that let you train your models like AWS or Google Cloud Coogle Colab -- Google Colab gives you free access to a tesla K80 GPU for 12 hours at a time. Once 12 hours have ellapsed you can just reload and continue! The only limitation is that you have to upload the data to Google Drive and if the dataset is massive you may run out of space. However, once a model is trained then a normal CPU can be used for the predict.py file and you will have an answer within some seconds. Hyperparameters As you can see you have a wide selection of hyperparameters available and you can get even more by making small modifications to the code. Thus it may seem overly complicated to choose the right ones especially if the training needs at least 15 minutes to be completed. So here are some hints: By increasing the number of epochs the accuracy of the network on the training set gets better and better however be careful because if you pick a large number of epochs the network won't generalize well, that is to say it will have high accuracy on the training image and low accuracy on the test images. Eg: training for 12 epochs training accuracy: 85% Test accuracy: 82%. Training for 30 epochs training accuracy 95% test accuracy 50%. A big learning rate guarantees that the network will converge fast to a small error but it will constantly overshot A small learning rate guarantees that the network will reach greater accuracies but the learning process will take longer Densenet121 works best for images but the training process takes significantly longer than alexnet or vgg16 *My settings were lr=0.001, dropoup=0.5, epochs= 15 and my test accuracy was 86% with densenet121 as my feature extraction model. Pre-Trained Network The checkpoint.pth file contains the information of a network trained to recognise 102 different species of flowers. I has been trained with specific hyperparameters thus if you don't set them right the network will fail. In order to have a prediction for an image located in the path /path/to/image using my pretrained model you can simply type python predict.py /path/to/image checkpoint.pth Contributing Please read CONTRIBUTING.md for the process for submitting pull requests. Authors Shanmukha Mudigonda - Initial work Udacity - Final Project of the AI with Python Nanodegree

This repository contains the code for building an image classifier that can identify different species of flowers. This was build on pytorch deep learning framework and using python. This project was completed as part of AI Programming with Python Nanodegree program (Udacity).

Image Classification for a City Dog Show Project Goal Improving your programming skills using Python In this project you will use a created image classifier to identify dog breeds. We ask you to focus on Python and not on the actual classifier (We will focus on building a classifier ourselves later in the program). Description: Your city is hosting a citywide dog show and you have volunteered to help the organizing committee with contestant registration. Every participant that registers must submit an image of their dog along with biographical information about their dog. The registration system tags the images based upon the biographical information. Some people are planning on registering pets that aren’t actual dogs. You need to use an already developed Python classifier to make sure the participants are dogs. Note, you DO NOT need to create the classifier. It will be provided to you. You will need to apply the Python tools you just learned to USE the classifier. Your Tasks: Using your Python skills, you will determine which image classification algorithm works the "best" on classifying images as "dogs" or "not dogs". Determine how well the "best" classification algorithm works on correctly identifying a dog's breed. If you are confused by the term image classifier look at it simply as a tool that has an input and an output. The Input is an image. The output determines what the image depicts. (for example: a dog). Be mindful of the fact that image classifiers do not always categorize the images correctly. (We will get to all those details much later on the program). Time how long each algorithm takes to solve the classification problem. With computational tasks, there is often a trade-off between accuracy and runtime. The more accurate an algorithm, the higher the likelihood that it will take more time to run and use more computational resources to run. For further clarifications, please check our FAQs here. Important Notes: For this image classification task you will be using an image classification application using a deep learning model called a convolutional neural network (often abbreviated as CNN). CNNs work particularly well for detecting features in images like colors, textures, and edges; then using these features to identify objects in the images. You'll use a CNN that has already learned the features from a giant dataset of 1.2 million images called ImageNet. There are different types of CNNs that have different structures (architectures) that work better or worse depending on your criteria. With this project you'll explore the three different architectures (AlexNet, VGG, and ResNet) and determine which is best for your application. We have provided you with a classifier function in classifier.py that will allow you to use these CNNs to classify your images. The test_classifier.py file contains an example program that demonstrates how to use the classifier function. For this project, you will be focusing on using your Python skills to complete these tasks using the classifier function; in the Neural Networks lesson you will be learning more about how these algorithms work. Remember that certain breeds of dog look very similar. The more images of two similar looking dog breeds that the algorithm has learned from, the more likely the algorithm will be able to distinguish between those two breeds. We have found the following breeds to look very similar: Great Pyrenees and Kuvasz, German Shepherd and Malinois, Beagle and Walker Hound, amongst others.

Image classification from scratch, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made Keras Application model to demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset

Class Project - Building an image recognition classifier

Building an image classifier to detect the distraction levels of a driver

[Deep Learning] Building an image classifier application using Pytorch - a project from Data Scientist NanoDegree

A repository to perform data collection of images using web scraping techniques to create a dataset from the IKEA website and building an image classifier to classify images into 4 categories - beds, tables, chairs, and tableware.

It's a simple convolutional neural network

A simple demonstration of building an image classifier with Scikit-learn

Using Jupyter notebook for building an image classifier with Python and Tensorflow.

Building an image classifier for natural disaster damage assessment using social media and google images

Building an image classifier on animal images using Keras and pretrained CNN GoogleInceptionV3 on ImageNet dataset in an online competition.

This repository is about building an Image classifier CNN with Python on Plant Disease Prediction

Building an application that can train an Image classifier on a dataset, then predict a new image using the trained model.

Building a python application that can train an image classifier on data-set, and then predict new images using the trained model. the

This is an end-to-end Deep Learning project that works on unstructured data by building a multi-class image classifier using TensorFlow 2.15 and TensorFlow Hub.

Image Classification is the task of assigning a label to an image from a predefined set of classes. building a simple image classifier for common everyday objects using a Convolutional Neural Network (CNN) with TensorFlow/Keras, trained on a custom data.

Handwriting recognition is a common problem encountered in automating day to day tasks like identifying postal addresses on envelops, number plate for vehicles etc. A complete handwriting recognition system handles formatting, performs correct segmentation, entails character recognition etc. This project aims to solve the problem of character recognition by building a classifier which can use the scanned pixel data of an image to identify the character.

There are estimated to be nearly half a million species of plant in the world. Classification of species has been historically problematic and often results in duplicate identifications. The objective of this playground competition is to use binary leaf images and extracted features, including shape, margin & texture, to accurately identify 99 species of plants. Leaves, due to their volume, prevalence, and unique characteristics, are an effective means of differentiating plant species. They also provide a fun introduction to applying techniques that involve image-based features. As a first step, try building a classifier that uses the provided pre-extracted features. Next, try creating a set of your own features. Finally, examine the errors you're making and see what you can do to improve.

4.3.1 Abstract - Classification problem has an effective role in self-driving cars. However, classifying weather the showing object is a car, traffic sign, or building is needed, but "how it can be implemented?" This takes many phases. - Traffic sign recognition is a multi-class classification problem. Traffic signs can provide a wide range of variations between classes in terms of color, shape, and the presence of pictograms or text. However, there exist subsets of classes (e. g., speed limit signs) that are very similar to each other. - The classifier has to cope with large variations in visual appearances due to illumination changes, partial occlusions, rotations, weather conditions, etc. - Humans are capable of recognizing the large variety of existing road signs with close to 100% correctness. This does not only apply to real-world driving, which provides both context and multiple views of a single traffic sign, but also to the recognition from single images. - In this project we will focus on the mechanism of recognizing the traffic signs and differentiating between them. In order to achieve this, several steps had been taken.

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