Unofficial implementation of Fast-SCNN: Fast Semantic Segmentation Network

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Deep Learning Fast-SCNN
Overview

Fast-SCNN: Fast Semantic Segmentation Network

Unofficial implementation of the model architecture of Fast-SCNN. Real-time Semantic Segmentation and mobile friendly memory consumption.

Tested with Python 3.6 and Pytorch '1.0.1.post2'

Network Architecture image from the paper

Example

from fast_scnn import Fast_SCNN
model = Fast_SCNN(input_channel=3, num_classes=10)

Results with Fast-SCNN

Inference Speed (fps) on Cityscapes

Inference Speed

mIoU on Cityscapes

mIoU

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Comments
  • Bdd100k and custom dataset inference

    Bdd100k and custom dataset inference

    @DeepVoltaire Thanks for open sourcing the source code , its a great work . I have few queries Q1 when i used the pre-trained model on BDD100k and on custom dataset i get the below results image Q2 should we use any different kind of preprocessing technique on bdd100k and custom dataset to obtain results like cityscapes Q3 any idea to increase the mask quality for poles and get the trunk part of tree more clearly Q4 y does sky getting classified and segmented as buildings or trees

    THanks in advance

    opened by abhigoku10 1
  • got wrong code

    got wrong code

    https://github.com/DeepVoltaire/Fast-SCNN/blob/fbd87e614fe70b0bb35c6c0a647a3f8158b78542/fast_scnn.py#L162 you write the self.sconv1 twice here, is that right?

    opened by deadpoppy 0
  • dimension problem

    dimension problem

    Do someone know how to solve this problem?---- "File "models/fast_scnn.py", line 149, in forward x = torch.add(high_res_input, low_res_input) RuntimeError: The size of tensor a (90) must match the size of tensor b (92) at non-singleton dimension 2"

    opened by ashergaga 2
Owner
Philip Popien
Deep Learning Engineer focused on Computer Vision applications. Effective Altruist.
Philip Popien
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