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PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision

@misc{CV2018,
  author =       {Donny You (youansheng@gmail.com)},
  howpublished = {\url{https://github.com/donnyyou/PyTorchCV}},
  year =         {2018}
}

This repository provides source code for some deep learning based cv problems. We'll do our best to keep this repository up to date. If you do find a problem about this repository, please raise it as an issue. We will fix it immediately.

Implemented Papers

• Image Classification

  • VGG: Very Deep Convolutional Networks for Large-Scale Image Recognition
  • ResNet: Deep Residual Learning for Image Recognition
  • DenseNet: Densely Connected Convolutional Networks
  • ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
  • ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design

• Semantic Segmentation

  • DeepLabV3: Rethinking Atrous Convolution for Semantic Image Segmentation
  • PSPNet: Pyramid Scene Parsing Network
  • DenseASPP: DenseASPP for Semantic Segmentation in Street Scenes

• Object Detection

  • SSD: Single Shot MultiBox Detector
  • Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
  • YOLOv3: An Incremental Improvement
  • FPN: Feature Pyramid Networks for Object Detection

• Pose Estimation

  • CPM: Convolutional Pose Machines
  • OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

• Instance Segmentation

  • Mask R-CNN

Performances with PyTorchCV

Image Classification

• ResNet: Deep Residual Learning for Image Recognition

Semantic Segmentation

• PSPNet: Pyramid Scene Parsing Network

Object Detection

• SSD: Single Shot MultiBox Detector

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

• YOLOv3: An Incremental Improvement

Pose Estimation

• OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

Instance Segmentation

• Mask R-CNN

Commands with PyTorchCV

Take PSPNet as an example. ("tag" could be any string, include an empty one.)

• Training

cd scripts/seg/cityscapes/
bash run_fs_pspnet_cityscapes_seg.sh train tag

• Resume Training

cd scripts/seg/cityscapes/
bash run_fs_pspnet_cityscapes_seg.sh train tag

• Validate

cd scripts/seg/cityscapes/
bash run_fs_pspnet_cityscapes_seg.sh val tag

• Testing:

cd scripts/seg/cityscapes/
bash run_fs_pspnet_cityscapes_seg.sh test tag

Examples with PyTorchCV