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Title: GitHub - YimanTech/Detectron: FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

Open Graph Title: GitHub - YimanTech/Detectron: FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

X Title: GitHub - YimanTech/Detectron: FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

Description: FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet. - YimanTech/Detectron

Open Graph Description: FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet. - YimanTech/Detectron

X Description: FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet. - YimanTech/Detectron

Opengraph URL: https://github.com/YimanTech/Detectron

X: @github

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Apache-2.0 licensehttps://patch-diff.githubusercontent.com/YimanTech/Detectron
https://patch-diff.githubusercontent.com/YimanTech/Detectron#detectron
Mask R-CNNhttps://arxiv.org/abs/1703.06870
Caffe2https://github.com/caffe2/caffe2
Feature Pyramid Networks for Object Detectionhttps://arxiv.org/abs/1612.03144
Mask R-CNNhttps://arxiv.org/abs/1703.06870
Detecting and Recognizing Human-Object Interactionshttps://arxiv.org/abs/1704.07333
Focal Loss for Dense Object Detectionhttps://arxiv.org/abs/1708.02002
Non-local Neural Networkshttps://arxiv.org/abs/1711.07971
Learning to Segment Every Thinghttps://arxiv.org/abs/1711.10370
Data Distillation: Towards Omni-Supervised Learninghttps://arxiv.org/abs/1712.04440
DensePose: Dense Human Pose Estimation In The Wildhttps://arxiv.org/abs/1802.00434
Group Normalizationhttps://arxiv.org/abs/1803.08494
https://patch-diff.githubusercontent.com/YimanTech/Detectron/blob/master/demo/output/33823288584_1d21cf0a26_k_example_output.jpg
https://patch-diff.githubusercontent.com/YimanTech/Detectron#introduction
Mask R-CNNhttps://arxiv.org/abs/1703.06870
RetinaNethttps://arxiv.org/abs/1708.02002
Faster R-CNNhttps://arxiv.org/abs/1506.01497
RPNhttps://arxiv.org/abs/1506.01497
Fast R-CNNhttps://arxiv.org/abs/1504.08083
R-FCNhttps://arxiv.org/abs/1605.06409
ResNeXt{50,101,152}https://arxiv.org/abs/1611.05431
ResNet{50,101,152}https://arxiv.org/abs/1512.03385
Feature Pyramid Networkshttps://arxiv.org/abs/1612.03144
VGG16https://arxiv.org/abs/1409.1556
Referenceshttps://patch-diff.githubusercontent.com/YimanTech/Detectron#references
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https://patch-diff.githubusercontent.com/YimanTech/Detectron#references
Data Distillation: Towards Omni-Supervised Learninghttps://arxiv.org/abs/1712.04440
Learning to Segment Every Thinghttps://arxiv.org/abs/1711.10370
Non-Local Neural Networkshttps://arxiv.org/abs/1711.07971
Mask R-CNNhttps://arxiv.org/abs/1703.06870
Focal Loss for Dense Object Detectionhttps://arxiv.org/abs/1708.02002
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourhttps://arxiv.org/abs/1706.02677
Detecting and Recognizing Human-Object Interactionshttps://arxiv.org/abs/1704.07333
Feature Pyramid Networks for Object Detectionhttps://arxiv.org/abs/1612.03144
Aggregated Residual Transformations for Deep Neural Networkshttps://arxiv.org/abs/1611.05431
R-FCN: Object Detection via Region-based Fully Convolutional Networkshttp://arxiv.org/abs/1605.06409
Deep Residual Learning for Image Recognitionhttp://arxiv.org/abs/1512.03385
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkshttp://arxiv.org/abs/1506.01497
Fast R-CNNhttp://arxiv.org/abs/1504.08083
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