Deep Learning Specialization - Coursera
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  • Introduction
  • Neural Networks and Deep Learning
    • Introduction to Deep Learning
    • Logistic Regression as a Neural Network (Neural Network Basics)
    • Shallow Neural Network
    • Deep Neural Network
  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
    • Practical Aspects of Deep Learning
    • Optimization Algorithms
    • Hyperparameter Tuning, Batch Normalization and Programming Frameworks
  • Structuring Machine Learning Projects
    • Introduction to ML Strategy
    • Setting Up Your Goal
    • Comparing to Human-Level Performance
    • Error Analysis
    • Mismatched Training and Dev/Test Set
    • Learning from Multiple Tasks
    • End-to-End Deep Learning
  • Convolutional Neural Networks
    • Foundations of Convolutional Neural Networks
    • Deep Convolutional Models: Case Studies
      • Classic Networks
      • ResNets
      • Inception
    • Advice for Using CNNs
    • Object Detection
      • Object Localization
      • Landmark Detection
      • Sliding Window Detection
      • The YOLO Algorithm
      • Intersection over Union
      • Non-Max Suppression
      • Anchor Boxes
      • Region Proposals
    • Face Recognition
      • One-Shot Learning
      • Siamese Network
      • Face Recognition as Binary Classification
    • Neural Style Transfer
  • Sequence Models
    • Recurrent Neural Networks
      • RNN Structure
      • Types of RNNs
      • Language Modeling
      • Vanishing Gradient Problem in RNNs
      • Gated Recurrent Units (GRUs)
      • Long Short-Term Memory Network (LSTM)
      • Bidirectional RNNs
    • Natural Language Processing & Word Embeddings
      • Introduction to Word Embeddings
      • Learning Word Embeddings: Word2Vec and GloVe
      • Applications using Word Embeddings
      • De-Biasing Word Embeddings
    • Sequence Models & Attention Mechanisms
      • Sequence to Sequence Architectures
        • Basic Models
        • Beam Search
        • Bleu Score
        • Attention Model
      • Speech Recognition
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  1. Convolutional Neural Networks
  2. Object Detection

Object Localization

PreviousObject DetectionNextLandmark Detection

Last updated 4 years ago

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Object Localization is the process of locating an object in an image, and creating a bounding box around the object once localized.

Classification with Localization

We use CNNs for object classification. However, they can also be used for localization simultaneously.

To do this, we must add the parameters bx,by,bh,bwb_x, b_y, b_h, b_wbx​,by​,bh​,bw​ to the softmax output where (bx,by)(b_x, b_y)(bx​,by​) are the coordinates for the center of the required bounding box and bh,bwb_h, b_wbh​,bw​ are its height and width respectively.

Note that the training images must contain bounding boxes too (with the 4 parameters) so as to be able to learn the parameters.

In fact, every training image has the following vector associated with it:

[p,bx,by,bh,bw,cp, b_x, b_y, b_h, b_w, cp,bx​,by​,bh​,bw​,c]

where p=1 if there is an object in the image and c is the label of the object.

If p=0 (no object in the image), then the vector becomes [0, ?, ?, ?, ?, ?] where ?s denote "don't-care" values.

(c can be one-hot encoded).