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Implementation of various Deep Image Segmentation models in keras. .Deep learning and convolutional neural networks (CNN) have been extremely ubiquitous in the field of computer vision. CNNs are popular for several computer vision tasks such as Image Classification, Object Detection, Image Generation, etc. Like for all other computer vision task…

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Image Segmentation Keras : Implementation of Segnet, FCN, UNet, PSPNet and other models in Keras.

  • Open the folder to view the code

Implementation of various Deep Image Segmentation models in keras.

Working Google Colab Examples:

Our Other Repositories

Models

Following models are supported:

model_name Base Model Segmentation Model
fcn_8 Vanilla CNN FCN8
fcn_32 Vanilla CNN FCN8
fcn_8_vgg VGG 16 FCN8
fcn_32_vgg VGG 16 FCN32
fcn_8_resnet50 Resnet-50 FCN32
fcn_32_resnet50 Resnet-50 FCN32
fcn_8_mobilenet MobileNet FCN32
fcn_32_mobilenet MobileNet FCN32
pspnet Vanilla CNN PSPNet
vgg_pspnet VGG 16 PSPNet
resnet50_pspnet Resnet-50 PSPNet
unet_mini Vanilla Mini CNN U-Net
unet Vanilla CNN U-Net
vgg_unet VGG 16 U-Net
resnet50_unet Resnet-50 U-Net
mobilenet_unet MobileNet U-Net
segnet Vanilla CNN Segnet
vgg_segnet VGG 16 Segnet
resnet50_segnet Resnet-50 Segnet
mobilenet_segnet MobileNet Segnet

Example results for the pre-trained models provided :

Input Image Output Segmentation Image

Prerequisites

  • Keras ( recommended version : 2.4.3 )

  • OpenCV for Python

  • Tensorflow ( recommended version : 2.4.1 )

    out_fname="out.png" )

Download the sample prepared dataset

Download and extract the following:

https://drive.google.com/file/d/0B0d9ZiqAgFkiOHR1NTJhWVJMNEU/view?usp=sharing

You will get a folder named dataset1/

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Implementation of various Deep Image Segmentation models in keras. .Deep learning and convolutional neural networks (CNN) have been extremely ubiquitous in the field of computer vision. CNNs are popular for several computer vision tasks such as Image Classification, Object Detection, Image Generation, etc. Like for all other computer vision task…

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