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DLIP
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  • Image Processing Basics
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      • Thresholding
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  • Deep Learning for Perception
    • Notes
      • Lane Detection with Deep Learning
      • Overview of Deep Learning
        • Object Detection
        • Deep Learning Basics: Introduction
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      • Bias vs Variance
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      • Object Detection
      • DL Techniques
        • Technical Strategy by A.Ng
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      • Tutorial: Install PyTorch
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          • Tutorial: Train Yolo v5 with custom dataset
        • Tutorial: Yolov5 in Pytorch (VS code)
        • Tutorial: Yolov3 in Keras
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      • Assignment: CNN Classification
      • Assignment: Object Detection
      • LAB: CNN Object Detection 1
      • LAB: CNN Object Detection 2
      • LAB Grading Criteria
    • Tutorial- Keras
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      • Digital Door Lock Control with Face Recognition
      • People Counting with YOLOv4 and DeepSORT
      • Eye Blinking Detection Alarm
      • Helmet-Detection Using YOLO-V5
      • Mask Detection using YOLOv5
      • Parking Space Management
      • Vehicle, Pedestrian Detection with IR Image
      • Drum Playing Detection
      • Turtle neck measurement program using OpenPose
    • DLIP 2022 Projects
      • BakeryCashier
      • Virtual Mouse
      • Sudoku Program with Hand gesture
      • Exercise Posture Assistance System
      • People Counting Embedded System
      • Turtle neck measurement program using OpenPose
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  • Resources
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On this page
  • Techniques
  • Hyperparameter Tuning
  • Methods of Efficient Inference
  • Pruning Deep Network
  • 1x1D CNN
  • Code Template - PyTorch

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  1. Deep Learning for Perception
  2. Notes

DL Techniques

PreviousObject DetectionNextTechnical Strategy by A.Ng

Last updated 3 years ago

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Techniques

Hyperparameter Tuning

Methods of Efficient Inference

  • Pruning

  • Weight Sharing

  • Quantization

  • Low-Rank Approximation

  • Binary / Ternary Net

  • Winograd Transformation

Pruning Deep Network

Weight pruning

  • Set individual weights in the weight matrix to zero. This corresponds to deleting connections as in the figure above.

Unit/Neuron pruning

  • Set entire columns to zero in the weight matrix to zero, in effect deleting the corresponding output neuron

1x1D CNN

  • 1×1 convolutions are an essential part of the Inception module.

  • A 1×1 convolution returns an output image with the same dimensions as the input image.

  • Colored images have three dimensions, or channels. 1×1 convolutions compress these channels at little cost, leaving a two-dimensional image to perform expensive 3×3 and 5×5 convolutions on.

  • Convolutional layers learn many filters to identify attributes of images. 1×1 convolutions can be placed as ‘bottlenecks’ to help compress a high number of filters into just the amount of information that is necessary for a classification.

Code Template - PyTorch

[Lecun et al. NIPS’89] [Han et al. NIPS’15]
Image for post
Image for Source: Inception v3 paper, image free to share.post

Read: Inception paper \[“Going deeper with convolutions”\]()

Template 1: the template is

https://arxiv.org/pdf/1409.4842.pdf
here
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