https://github.com/ajithvcoder/getsetgo_keras-beginner
Pretrained Convolutional Neural Network models(CNN) (Imagenet challange ILSVRC) are used to predict input image .Codes to extract specific layers ,fine tune and visualize those models are also provided.
https://github.com/ajithvcoder/getsetgo_keras-beginner
cnn-keras colab-notebook google-colab pretrained-models python3
Last synced: 9 months ago
JSON representation
Pretrained Convolutional Neural Network models(CNN) (Imagenet challange ILSVRC) are used to predict input image .Codes to extract specific layers ,fine tune and visualize those models are also provided.
- Host: GitHub
- URL: https://github.com/ajithvcoder/getsetgo_keras-beginner
- Owner: ajithvcoder
- Created: 2018-04-19T02:43:19.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2019-03-16T20:10:05.000Z (almost 7 years ago)
- Last Synced: 2025-04-08T19:48:07.465Z (10 months ago)
- Topics: cnn-keras, colab-notebook, google-colab, pretrained-models, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 289 KB
- Stars: 9
- Watchers: 3
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Using pretrained Convolutional Neural Network models(CNN) in Keras
### Credits
> -https://mlblr.com/
> -https://keras.io/
> -https://colab.research.google.com/
*codes used here are executed and tested only in colab.research.google.com
please make sure you have enabled GPU access from notebook settings for faster execution process*
### CNN models
Models used here are pretrained on ImageNet dataset ImageNet Large Scale Visual Recognition Challenge (ILSVRC) with python and keras deep learning library.
1.Resnet50 model
- Classifying input image with probabilities [notebook link](https://github.com/ajithvallabai/getsetgo_keras-beginner/blob/master/notebooks/Resnet50_imagenet_prediction.ipynb)
- Visualizing resnet50 model [notebook link](https://github.com/ajithvallabai/getsetgo_keras-beginner/blob/master/notebooks/Resnet50_visualization.ipynb)
2.VGG16 model
- Classifying input image with probabilities [notebook link](https://github.com/ajithvallabai/getsetgo_keras-beginner/blob/master/notebooks/Vgg16_imagenet_prediction.ipynb)
- Visuvalizing vgg16 model [notebook link](https://github.com/ajithvallabai/getsetgo_keras-beginner/blob/master/notebooks/Vgg16_visualization.ipynb)
3.VGG19 model
- Classifying input image with probabilities [notebook link](https://github.com/ajithvallabai/getsetgo_keras-beginner/blob/master/notebooks/VGG19_imagenet_prediction.ipynb)
- Extracting last layer of vgg19 and using them to predict [notebook link](https://github.com/ajithvallabai/getsetgo_keras-beginner/blob/master/notebooks/VGG19_imagenet_extractinglayer.ipynb)
- Visuvalizing model extracted model [notebook link](https://github.com/ajithvallabai/getsetgo_keras-beginner/blob/master/notebooks/VGG19_visualization.ipynb)
4.InceptionV3 model
- Classifying input image with probabilities [notebook link](https://github.com/ajithvallabai/getsetgo_keras-beginner/blob/master/notebooks/Inceptionv3_extractinglayer.ipynb)
- Extracting last layer of vgg19 and using them to predict [notebook link](https://github.com/ajithvallabai/getsetgo_keras-beginner/blob/master/notebooks/Inceptionv3_extractinglayer.ipynb)
- Visuvalizing model extracted model [notebook link](https://github.com/ajithvallabai/getsetgo_keras-beginner/blob/master/notebooks/Inceptionv3_vizualizing.ipynb)
**What is a pretrained model?**
Models that has been already trained on a particular data set with number of classes
**Use of pretrained model:**
Instead of training from scratch/starting with random weight initialiation pre-trained
models can be used with other datasets.
[keywords: fine tuning-transfer learning]
### Model info:
*Resnet50 model*
Architecture speciality:Use of residual blocks(skip connections) enabled training much deeper
network by handling vanishing and exploding gradient problems
https://arxiv.org/abs/1512.03385
*VGG16 model*
Architecture speciality: Use of 3x3 convolution filters with depth of 16 weight layers
https://arxiv.org/abs/1409.1556
*VGG19 model*
Architecture speciality: Use of 3x3 convolution filters with depth of 19 weight layers
https://arxiv.org/abs/1409.1556
*InceptionV3 model*
Architecture speciality:1x1,3x3,5x5,max pooling convolutions performed with less computation cost-sparsely deep connected network-Hebbian principle(neurons that fire together wire
together)
https://arxiv.org/pdf/1409.4842.pdf