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

@misc{CV2018,
  author =       {Donny You},
  howpublished = {\url{https://github.com/CVBox/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
  • MobileNetV2: Inverted Residuals and Linear Bottlenecks
  • ResNeXt: Aggregated Residual Transformations for Deep Neural Networks
  • SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
  • ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
  • ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design

• Pose Estimation

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

• 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

• Semantic Segmentation

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

• Instance Segmentation

  • Mask R-CNN

Performances with PyTorchCV

Object Detection

• SSD: Single Shot MultiBox Detector [SSD300, SSD512]

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

• YOLOv3: An Incremental Improvement

Commands with PyTorchCV

Take OpenPose as an example.

• Train the openpose model

python main.py  --hypes hypes/pose/coco/op_coco_pose.json \
                --base_lr 0.001 \
                --phase train \
                --gpu 0 1

• Finetune the openpose model

python main.py  --hypes hypes/pose/coco/op_coco_pose.json \
                --base_lr 0.001 \
                --phase train \
                --resume checkpoints/pose/coco/coco_open_pose_65000.pth \
                --gpu 0 1

• Test the openpose model(test_img):

python main.py  --hypes hypes/pose/coco/op_coco_pose.json \
                --phase test \
                --resume checkpoints/pose/coco/coco_open_pose_65000.pth \
                --test_img val/samples/ski.jpg \
                --gpu 0

• Test the openpose model(test_dir):

python main.py  --hypes hypes/pose/coco/op_coco_pose.json \
                --phase test \
                --resume checkpoints/pose/coco/coco_open_pose_65000.pth \
                --test_dir val/samples \
                --gpu 0

Examples with PyTorchCV

Example output of VGG19-OpenPose

Example output of VGG19-OpenPose