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Human-Segmentation-PyTorch

Human segmentation models, training/inference code, and trained weights, implemented in PyTorch.

Supported networks

To assess architecture, memory, forward time (in either cpu or gpu), numper of parameters, and number of FLOPs of a network, use this command:

python measure_model.py

Dataset

Portrait Segmentation (Human/Background)

Set

  • Python3.6.x is used in this repository.
  • Clone the repository:
git clone --recursive https://github.com/AntiAegis/Human-Segmentation-PyTorch.git
cd Human-Segmentation-PyTorch
git submodule sync
git submodule update --init --recursive
  • To install required packages, use pip:
workon humanseg
pip install -r requirements.txt
pip install -e models/pytorch-image-models

Training

  • For training a network from scratch, for example DeepLab3+, use this command:
python train.py --config config/config_DeepLab.json --device 0

where config/config_DeepLab.json is the configuration file which contains network, dataloader, optimizer, losses, metrics, and visualization configurations.

  • For resuming training the network from a checkpoint, use this command:
python train.py --config config/config_DeepLab.json --device 0 --resume path_to_checkpoint/model_best.pth
  • One can open tensorboard to monitor the training progress by enabling the visualization mode in the configuration file.

Inference

There are two modes of inference: video and webcam.

python inference_video.py --watch --use_cuda --checkpoint path_to_checkpoint/model_best.pth
python inference_webcam.py --use_cuda --checkpoint path_to_checkpoint/model_best.pth

Benchmark

  • Networks are trained on a combined dataset from the two mentioned datasets above. There are 6627 training and 737 testing images.
  • Input size of model is set to 320.
  • The CPU and GPU time is the averaged inference time of 10 runs (there are also 10 warm-up runs before measuring) with batch size 1.
  • The mIoU is measured on the testing subset (737 images) from the combined dataset.
  • Hardware configuration for benchmarking:
CPU: Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz
GPU: GeForce GTX 1050 Mobile, CUDA 9.0
Model Parameters FLOPs CPU time GPU time mIoU
UNet_MobileNetV2 (alpha=1.0, expansion=6) 4.7M 1.3G 167ms 17ms 91.37%
UNet_ResNet18 16.6M 9.1G 165ms 21ms 90.09%
DeepLab3+_ResNet18 16.6M 9.1G 133ms 28ms 91.21%
BiSeNet_ResNet18 11.9M 4.7G 88ms 10ms 87.02%
PSPNet_ResNet18 12.6M 20.7G 235ms 666ms ---
ICNet_ResNet18 11.6M 2.0G 48ms 55ms 86.27%