@misc{you2019torchcv,
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{https://github.com/donnyyou/torchcv}},
year = {2019}
}
Implemented Papers
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- 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
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- 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
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- 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
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- CPM: Convolutional Pose Machines
- OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
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- Mask R-CNN
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Generative Adversarial Networks
- Pix2pix: Image-to-Image Translation with Conditional Adversarial Nets
- CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.