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About the developer

PingoLH
147 Stars 35 Forks MIT License 24 Commits 39 Opened issues

Description

Fully Convolutional HarDNet for Segmentation in Pytorch

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FCHarDNet

Fully Convolutional HarDNet for Segmentation in Pytorch

Architecture

  • Simple U-shaped encoder-decoder structure
  • Conv3x3/Conv1x1 only (including the first layer)
  • No self-attention layer or Pyramid Pooling

Results

| Method | #Param
(M) | GMACs /
GFLOPs | Cityscapes
mIoU | fps on Titan-V
@1024x2048 | fps on 1080ti
@1024x2048 | | :---: | :---: | :---: | :---: | :---: | :---: | | ICNet | 7.7 | 30.7 | 69.5 | 63 | 48 | | SwiftNetRN-18 | 11.8 | 104 | 75.5 | - | 39.9 | | BiSeNet (1024x2048) | 13.4 | 119 | 77.7 | 36 | 27 | | BiSeNet (768x1536) | 13.4 | 66.8 | 74.7 | 72** | 54** | | FC-HarDNet-70 | 4.1 | 35.4 | 76.0 | 70 | 53 |

- ** Speed tested in 1536x768 instead of full resolution.

DataLoaders implemented

Requirements

  • pytorch >=0.4.0
  • torchvision ==0.2.0
  • scipy
  • tqdm
  • tensorboardX

Usage

Setup config file

Please see the usage section in meetshah1995/pytorch-semseg

To train the model :

python train.py [-h] [--config [CONFIG]]

--config Configuration file to use (default: hardnet.yml)

To validate the model :

usage: validate.py [-h] [--config [CONFIG]] [--model_path [MODEL_PATH]] [--save_image]
                       [--eval_flip] [--measure_time]

--config Config file to be used --model_path Path to the saved model --eval_flip Enable evaluation with flipped image | False by default --measure_time Enable evaluation with time (fps) measurement | True by default --save_image Enable writing result images to out_rgb (pred label blended images) and out_predID

Pretrained Weights

  • Cityscapes pretrained weights: Download
    (Val mIoU: 77.7, Test mIoU: 75.9)
  • Cityscapes pretrained with color jitter augmentation: Download
    (Val mIoU: 77.4, Test mIoU: 76.0)
  • HarDNet-Petite weights pretrained by ImageNet:
    included in weights/hardnetpetitebase.pth

Prediction Samples

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