This document summarizes and compares several popular image recognition algorithms: Local Binary Patterns (LBP), Haar classifier with OpenCV, and TensorFlow. It describes the basic methodology of LBP for texture analysis and feature extraction. It then explains how Haar classifiers use a cascade of boosted classifiers to perform real-time face detection. Finally, it discusses how TensorFlow's Inception model can be used for image classification and retraining the top layers for a custom dataset. The document provides examples and discusses the advantages and disadvantages of each approach.
Object classification using CNN & VGG16 Model (Keras and Tensorflow) Lalit Jain
Using CNN with Keras and Tensorflow, we have a deployed a solution which can train any image on the fly. Code uses Google Api to fetch new images, VGG16 model to train the model and is deployed using Python Django framework
One-shot learning is an object categorization problem in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images
Object classification using CNN & VGG16 Model (Keras and Tensorflow) Lalit Jain
Using CNN with Keras and Tensorflow, we have a deployed a solution which can train any image on the fly. Code uses Google Api to fetch new images, VGG16 model to train the model and is deployed using Python Django framework
One-shot learning is an object categorization problem in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images
Object extraction from satellite imagery using deep learningAly Abdelkareem
Presentation for extract objects from satellite imagery using deep learning techniques. you find a comparison between state-of-art approaches in computer vision.
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and ArchitecturesMLAI2
Regularization and transfer learning are two popular techniques to enhance generalization on unseen data, which is a fundamental problem of machine learning. Regularization techniques are versatile, as they are task- and architecture-agnostic, but they do not exploit a large amount of data available. Transfer learning methods learn to transfer knowledge from one domain to another, but may not generalize across tasks and architectures, and may introduce new training cost for adapting to the target task. To bridge the gap between the two, we propose a transferable perturbation, MetaPerturb, which is meta-learned to improve generalization performance on unseen data. MetaPerturb is implemented as a set-based lightweight network that is agnostic to the size and the order of the input, which is shared across the layers. Then, we propose a meta-learning framework, to jointly train the perturbation function over heterogeneous tasks in parallel. As MetaPerturb is a set-function trained over diverse distributions across layers and tasks, it can generalize to heterogeneous tasks and architectures. We validate the efficacy and generality of MetaPerturb trained on a specific source domain and architecture, by applying it to the training of diverse neural architectures on heterogeneous target datasets against various regularizers and fine-tuning. The results show that the networks trained with MetaPerturb significantly outperform the baselines on most of the tasks and architectures, with a negligible increase in the parameter size and no hyperparameters to tune.
Build a simple image recognition system with tensor flowDebasisMohanty37
A perfect working model to detect mnist dataset using TensorFlow.
Dataset:
http://yann.lecun.com/exdb/mnist/
For code check the below GitHub links:
https://github.com/Jitudebz/psychic-pancake
The goal of this report is the presentation of our biometry and security course’s project: Face recognition for Labeled Faces in the Wild dataset using Convolutional Neural Network technology with Graphlab Framework.
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...StampedeCon
In this session, we’ll discuss approaches for applying convolutional neural networks to novel computer vision problems, even without having millions of images of your own. Pretrained models and generic image data sets from Google, Kaggle, universities, and other places can be leveraged and adapted to solve industry and business specific problems. We’ll discuss the approaches of transfer learning and fine tuning to help anyone get started on using deep learning to get cutting edge results on their computer vision problems.
Object extraction from satellite imagery using deep learningAly Abdelkareem
Presentation for extract objects from satellite imagery using deep learning techniques. you find a comparison between state-of-art approaches in computer vision.
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and ArchitecturesMLAI2
Regularization and transfer learning are two popular techniques to enhance generalization on unseen data, which is a fundamental problem of machine learning. Regularization techniques are versatile, as they are task- and architecture-agnostic, but they do not exploit a large amount of data available. Transfer learning methods learn to transfer knowledge from one domain to another, but may not generalize across tasks and architectures, and may introduce new training cost for adapting to the target task. To bridge the gap between the two, we propose a transferable perturbation, MetaPerturb, which is meta-learned to improve generalization performance on unseen data. MetaPerturb is implemented as a set-based lightweight network that is agnostic to the size and the order of the input, which is shared across the layers. Then, we propose a meta-learning framework, to jointly train the perturbation function over heterogeneous tasks in parallel. As MetaPerturb is a set-function trained over diverse distributions across layers and tasks, it can generalize to heterogeneous tasks and architectures. We validate the efficacy and generality of MetaPerturb trained on a specific source domain and architecture, by applying it to the training of diverse neural architectures on heterogeneous target datasets against various regularizers and fine-tuning. The results show that the networks trained with MetaPerturb significantly outperform the baselines on most of the tasks and architectures, with a negligible increase in the parameter size and no hyperparameters to tune.
Build a simple image recognition system with tensor flowDebasisMohanty37
A perfect working model to detect mnist dataset using TensorFlow.
Dataset:
http://yann.lecun.com/exdb/mnist/
For code check the below GitHub links:
https://github.com/Jitudebz/psychic-pancake
The goal of this report is the presentation of our biometry and security course’s project: Face recognition for Labeled Faces in the Wild dataset using Convolutional Neural Network technology with Graphlab Framework.
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...StampedeCon
In this session, we’ll discuss approaches for applying convolutional neural networks to novel computer vision problems, even without having millions of images of your own. Pretrained models and generic image data sets from Google, Kaggle, universities, and other places can be leveraged and adapted to solve industry and business specific problems. We’ll discuss the approaches of transfer learning and fine tuning to help anyone get started on using deep learning to get cutting edge results on their computer vision problems.
Separating Hype from Reality in Deep Learning with Sameer FarooquiDatabricks
Deep Learning is all the rage these days, but where does the reality of what Deep Learning can do end and the media hype begin? In this talk, I will dispel common myths about Deep Learning that are not necessarily true and help you decide whether you should practically use Deep Learning in your software stack.
I’ll begin with a technical overview of common neural network architectures like CNNs, RNNs, GANs and their common use cases like computer vision, language understanding or unsupervised machine learning. Then I’ll separate the hype from reality around questions like:
• When should you prefer traditional ML systems like scikit learn or Spark.ML instead of Deep Learning?
• Do you no longer need to do careful feature extraction and standardization if using Deep Learning?
• Do you really need terabytes of data when training neural networks or can you ‘steal’ pre-trained lower layers from public models by using transfer learning?
• How do you decide which activation function (like ReLU, leaky ReLU, ELU, etc) or optimizer (like Momentum, AdaGrad, RMSProp, Adam, etc) to use in your neural network?
• Should you randomly initialize the weights in your network or use more advanced strategies like Xavier or He initialization?
• How easy is it to overfit/overtrain a neural network and what are the common techniques to ovoid overfitting (like l1/l2 regularization, dropout and early stopping)?
Using Deep Learning to Find Similar DressesHJ van Veen
Report by Luís Mey ( https://www.linkedin.com/in/lu%C3%ADs-gustavo-bernardo-mey-97b38927/ ) on Udacity Machine Learning Course - Final Project: Use Deep Learning to Find Similar Dresses.
This is the Bangla Handwritten Digit Recognition Report. you can see this report for your helping hand.
**Bengali is the world's fifth most spoken language, with 265 million native and non-native speakers accounting for 4% of the global population.
**Despite the large number of Bengali speakers, very little research has been conducted on Bangali handwritten digit recognition.
**The application of the BHwDR system is wide from postal code digit recognition to license plate recognition, digit recognition in cheques in the banking system to exam paper registration number recognition.
2. Local Binary Pattern with Sci-kit
Local Binary Patterns, or LBPs for short, are a texture descriptor made popular by
the work of Ojala.
If the current pixel value is greater or equal to the neighboring pixel value, the
corresponding bit in the binary array is set to 1 else if the current pixel value is less
than the neighboring pixel value, the corresponding bit in the binary array is set to
0.
9. Haar Classifier + OpenCV
Object Detection using Haar feature-based cascade classifiers is an effective
object detection method proposed by Paul Viola and Michael Jones in their paper,
"Rapid Object Detection using a Boosted Cascade of Simple Features" in 2001.
Initially, the algorithm needs a lot of positive images (images of faces) and
negative images (images without faces) to train the classifier. Then we need to
extract features from it.
10. Methods
Three tools to use – “createsamples”, “haartraining” and “performance”
CreateSamples
Tool from OpenCV to automate creation of training samples
Four functionalities
1. create training samples from one image applying distortions.
2. create training samples from a collection of images, without distortions.
3. create testing samples with ground truth from one image applying distortions.
4. show images from the .vec internal format which contains a collection of samples.
Best to use a combination of the functionalities to create many samples from
many images with distortions and merge them.
13. The software that performs the viola-jones algorithm and creates the cascade file
Sample run:
opencv_traincascade -data classifier -vec samples.vec -bg negatives.txt
-numStages 20 -minHitRate 0.999 -maxFalseAlarmRate 0.5 -numPos 1000
-numNeg 600 -w 80 -h 40 -mode ALL -precalcValBufSize 1024
“data” is the directory in which to store the output
“vec” is the .vec file containing the positive images
“bg” is a text file with a collection of paths to background (negative) images
“nstages” is the number of stages of boosting
“minhitrate” and “maxfalsealarm” are cutoff values for hit rate and false alarm, per stage
“npos” and “nneg” are the number of positive and negative images to be used from the given sets
“w” and “h” are the width and the height of the sample
“precalcValBufSize” buffer size to store the feature values
14. Advantages & Disadvantages
Advantages:
1. Easy to implement once classifier is ready.
Disadvantages:
1. Does not detect for all the images.
2. Training the classifier takes time.
3. Needs a lot of training data.
15. Tensorflow
Tensorflow is Google's open source deep learning library.
We will load the Inception-v3 model to generate descriptive labels for an image.
The Inception model is a deep convolutional neural network and was trained on
the ImageNet dataset, where the task was to classify images into 1000 classes.
16. Build Training
Place the directory with all the images in respective folder.
Give path of the directory while starting the training.
bazel build tensorflow/examples/image_retraining:retrain
bazel-bin/tensorflow/examples/image_retraining/retrain --image_dir ~/fruits
17. Advantages & Disadvantages
ADVANTAGES
1. Pre-trained classifier can be used.
2. Training on our classes doesn't take much time.
3. Has a good accuracy rate.
4. Can be trained with any variations of the image.
DISADVANTAGES
1. Image detection may go wrong with Inception.
2. Single class cannot be trained.