Li Jianshu's starred repositories

Arc2Face

Arc2Face: A Foundation Model of Human Faces

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unidiffuser

Code and models for the paper "One Transformer Fits All Distributions in Multi-Modal Diffusion"

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dlrover

DLRover: An Automatic Distributed Deep Learning System

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pwget

Similar to wget, but in parallel

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todo

A terminal todo list manager

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Anti-UAV

🔥🔥Official Repository for Anti-UAV🔥🔥

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AttentionGAN

AttentionGAN for Unpaired Image-to-Image Translation & Multi-Domain Image-to-Image Translation

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SelectionGAN

[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

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GestureGAN

[ACM MM 2018 Oral] GestureGAN for Hand Gesture-to-Gesture Translation in the Wild

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GloRe

Global Reasoning module for visual recognition

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iotex-core

Official implementation of IoTeX blockchain protocol in Go.

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Gravity-Simulation

Place planets, and watch them following physics laws !

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face.evoLVe

🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

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Augmentor

Image augmentation library in Python for machine learning.

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pretrained-models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

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Detectron.pytorch

A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron weights are available.

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fastSceneUnderstanding

segmentation, instance segmentation and single image depth

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gradient-checkpointing

Make huge neural nets fit in memory

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tf_train_with_generator_in_background_queue

This is an example code to realize the following functions: Train Tensorflow model with feed_dict. The data are generated with a generator. A Queue is used to run the generator in separate threads.

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Unsupervised-Aspect-Extraction

Code for acl2017 paper "An unsupervised neural attention model for aspect extraction"

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CRF-RNN_Tensorflow

Tensorflow implementation of CRF-RNN

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imgaug

Image augmentation for machine learning experiments.

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setGPU

Small Python library to automatically set CUDA_VISIBLE_DEVICES to the least loaded device on multi-GPU systems.

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