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

    author = {Ansheng You and Xiangtai Li and Zhen Zhu and Yunhai Tong},
    title = {TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision},
    howpublished = {\url{}},
    year = {2019}

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
    • Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search
  • Semantic Segmentation

    • DeepLabV3: Rethinking Atrous Convolution for Semantic Image Segmentation
    • PSPNet: Pyramid Scene Parsing Network
    • DenseASPP: DenseASPP for Semantic Segmentation in Street Scenes
    • Asymmetric Non-local Neural Networks for Semantic Segmentation
  • 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
  • Generative Adversarial Networks

    • Pix2pix: Image-to-Image Translation with Conditional Adversarial Nets
    • CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.
1 Like

Welcome your attention!!!

Nice work. Links are broken tho

I have fixed it. Thanks!