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rockXL's Projects

fcn.berkeleyvision.org icon fcn.berkeleyvision.org

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

gdlnotes icon gdlnotes

Google Deep Learning Notes(TensorFlow教程)

git icon git

Git Source Code Mirror - This is a publish-only repository and all pull requests are ignored. Please follow Documentation/SubmittingPatches procedure for any of your improvements.

labelimg icon labelimg

🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images

leetcode icon leetcode

:pencil: Python / C++ 11 Solutions of All 468 LeetCode Questions

lightgbm icon lightgbm

A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.

lstm-human-activity-recognition icon lstm-human-activity-recognition

Human activity recognition using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING).

py-faster-rcnn icon py-faster-rcnn

Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version

py-goturn icon py-goturn

This is the python implementation of single object tracking from GOTURN paper

py-r-fcn icon py-r-fcn

R-FCN with joint training and python support

pygoturn icon pygoturn

PyTorch implementation of GOTURN visual tracker

scnn icon scnn

Segment-CNN: A Framework for Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs

squeezenet icon squeezenet

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters

ss-tad icon ss-tad

End-to-End, Single-Stream Temporal Action Detection in Untrimmed Videos (Official Repo for SS-TAD)

tensorflow icon tensorflow

Computation using data flow graphs for scalable machine learning

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