📚
DLIP
  • Introduction
  • Prerequisite
  • Image Processing Basics
    • Notes
      • Thresholding
      • Spatial Filtering
      • Masking with Bitwise Operation
      • Model n Calibration
    • Tutorial
      • Tutorial: Install OpenCV C++
      • Tutorial: Create OpenCV Project
      • Tutorial: C++ basics
      • Tutorial: OpenCV Basics
      • Tutorial: Image Watch for Debugging
      • Tutorial: Spatial Filter
      • Tutorial: Thresholding and Morphology
      • Tutorial: Camera Calibration
      • Tutorial: Color Image Processing
      • Tutorial: Edge Line Circle Detection
      • Tutorial: Corner Detection and Optical Flow
      • Tutorial: OpenCV C++ Cheatsheet
      • Tutorial: Installation for Py OpenCV
      • Tutorial: OpenCv (Python) Basics
    • LAB
      • Lab Report Template
      • Lab Report Grading Criteria
      • LAB Report Instruction
      • LAB: Grayscale Image Segmentation
        • LAB: Grayscale Image Segmentation -Gear
        • LAB: Grayscale Image Segmentation - Bolt and Nut
      • LAB: Color Image Segmentation
        • LAB: Facial Temperature Measurement with IR images
        • LAB: Magic Cloak
      • LAB: Straight Lane Detection and Departure Warning
      • LAB: Dimension Measurement with 2D camera
      • LAB: Tension Detection of Rolling Metal Sheet
  • Deep Learning for Perception
    • Notes
      • Lane Detection with Deep Learning
      • Overview of Deep Learning
        • Object Detection
        • Deep Learning Basics: Introduction
        • Deep Learning State of the Art
        • CNN, Object Detection
      • Perceptron
      • Activation Function
      • Optimization
      • Convolution
      • CNN Overview
      • Evaluation Metric
      • LossFunction Regularization
      • Bias vs Variance
      • BottleNeck Unit
      • Object Detection
      • DL Techniques
        • Technical Strategy by A.Ng
    • Tutorial - PyTorch
      • Tutorial: Install PyTorch
      • Tutorial: Python Numpy
      • Tutorial: PyTorch Tutorial List
      • Tutorial: PyTorch Example Code
      • Tutorial: Tensorboard in Pytorch
      • Tutorial: YOLO in PyTorch
        • Tutorial: Yolov8 in PyTorch
        • Tutorial: Train Yolo v8 with custom dataset
          • Tutorial: Train Yolo v5 with custom dataset
        • Tutorial: Yolov5 in Pytorch (VS code)
        • Tutorial: Yolov3 in Keras
    • LAB
      • Assignment: CNN Classification
      • Assignment: Object Detection
      • LAB: CNN Object Detection 1
      • LAB: CNN Object Detection 2
      • LAB Grading Criteria
    • Tutorial- Keras
      • Train Dataset
      • Train custom dataset
      • Test model
      • LeNet-5 Tutorial
      • AlexNet Tutorial
      • VGG Tutorial
      • ResNet Tutorial
    • Resource
      • Online Lecture
      • Programming tutorial
      • Books
      • Hardware
      • Dataset
      • Useful sites
  • Must Read Papers
    • AlexNet
    • VGG
    • ResNet
    • R-CNN, Fast-RCNN, Faster-RCNN
    • YOLOv1-3
    • Inception
    • MobileNet
    • SSD
    • ShuffleNet
    • Recent Methods
  • DLIP Project
    • Report Template
    • DLIP 2021 Projects
      • 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
    • DLIP Past Projects
  • Installation Guide
    • Installation Guide for Pytorch
      • Installation Guide 2021
    • Anaconda
    • CUDA cuDNN
      • CUDA 10.2
    • OpenCV
      • OpenCV Install and Setup
        • OpenCV 3.4.13 with VS2019
        • OpenCV3.4.7 VS2017
        • MacOS OpenCV C++ in XCode
      • Python OpenCV
      • MATLAB-OpenCV
    • Framework
      • Keras
      • TensorFlow
        • Cheat Sheet
        • Tutorial
      • PyTorch
    • IDE
      • Visual Studio Community
      • Google Codelab
      • Visual Studio Code
        • Python with VS Code
        • Notebook with VS Code
        • C++ with VS Code
      • Jupyter Notebook
        • Install
        • How to use
    • Ubuntu
      • Ubuntu 18.04 Installation
      • Ubuntu Installation using Docker in Win10
      • Ubuntu Troubleshooting
    • ROS
  • Programming
    • Python_Numpy
      • Python Tutorial - Tips
      • Python Tutorial - For Loop
      • Python Tutorial - List Tuple, Dic, Set
    • Markdown
      • Example: API documentation
    • Github
      • Create account
      • Tutorial: Github basic
      • Tutorial: Github Desktop
    • Keras
      • Tutorial Keras
      • Cheat Sheet
    • PyTorch
      • Cheat Sheet
      • Autograd in PyTorch
      • Simple ConvNet
      • MNIST using LeNet
      • Train ConvNet using CIFAR10
  • Resources
    • Useful Resources
    • Github
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On this page
  • Keras.io Tutorial
  • Pyimagesearch.com Tutorial
  • Keras Model Zoo:
  • YOLOv3 in Keras
  • Other Tutorials
  • Exercise
  • Image Recognition
  • Popular network architecture in Keras
  • Available models

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  1. Programming
  2. Keras

Tutorial Keras

PreviousKerasNextCheat Sheet

Last updated 3 years ago

Was this helpful?

Follow the tutorials in the following orders

Keras.io Tutorial

Computer Vision Tutorials:

  1. Simple MNIST CNN

    • modified tutorial

      • ,

    • original tutorial

      • ,

  2. Image classification from scratch

    • modified tutorial

    • original tutorial

      • ,

Pyimagesearch.com Tutorial

Get Started Tutorial

Keras Model Zoo:

YOLOv3 in Keras

Other Tutorials

Exercise

Image Recognition

  1. Use AlexNet on MNIST

  2. Use LeNet to train and recognize vehicles. Use Dataset of

  3. Create your own convnet network for MNIST dataset. Compare the performance with tutorial output

Popular network architecture in Keras

Available models

Model
Size
Top-1 Accuracy
Top-5 Accuracy
Parameters
Depth

88 MB

0.790

0.945

22,910,480

126

528 MB

0.713

0.901

138,357,544

23

549 MB

0.713

0.900

143,667,240

26

98 MB

0.749

0.921

25,636,712

-

171 MB

0.764

0.928

44,707,176

-

232 MB

0.766

0.931

60,419,944

-

98 MB

0.760

0.930

25,613,800

-

171 MB

0.772

0.938

44,675,560

-

232 MB

0.780

0.942

60,380,648

-

92 MB

0.779

0.937

23,851,784

159

215 MB

0.803

0.953

55,873,736

572

16 MB

0.704

0.895

4,253,864

88

14 MB

0.713

0.901

3,538,984

88

33 MB

0.750

0.923

8,062,504

121

57 MB

0.762

0.932

14,307,880

169

80 MB

0.773

0.936

20,242,984

201

23 MB

0.744

0.919

5,326,716

-

343 MB

0.825

0.960

88,949,818

-

29 MB

-

-

5,330,571

-

31 MB

-

-

7,856,239

-

36 MB

-

-

9,177,569

-

48 MB

-

-

12,320,535

-

75 MB

-

-

19,466,823

-

118 MB

-

-

30,562,527

-

166 MB

-

-

43,265,143

-

256 MB

-

-

66,658,687

-

The top-1 and top-5 accuracy refers to the model's performance on the ImageNet validation dataset.

Depth refers to the topological depth of the network. This includes activation layers, batch normalization layers etc.

Source code by

https://keras.io/examples/vision/
source from github
run on colab
source from github
run on colab
source from github
source from github
run on colab
How to train and test your own dataset
Collection of pretrained models: click here
YOLOv3 in Keras
SSD in Keras
Blog post
experiencor Huynh Ngoc Anh
Xception
VGG16
VGG19
ResNet50
ResNet101
ResNet152
ResNet50V2
ResNet101V2
ResNet152V2
InceptionV3
InceptionResNetV2
MobileNet
MobileNetV2
DenseNet121
DenseNet169
DenseNet201
NASNetMobile
NASNetLarge
EfficientNetB0
EfficientNetB1
EfficientNetB2
EfficientNetB3
EfficientNetB4
EfficientNetB5
EfficientNetB6
EfficientNetB7
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