zou jinyi (manutdzou)

manutdzou

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Company:Lenove Research AI Lab

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zou jinyi's repositories

manutdzou.github.io

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flash-attention

Fast and memory-efficient exact attention

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Lion

Lion: Adversarial Distillation of Closed-Source Large Language Model

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CLIP

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

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lama-cleaner

Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.

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GLIP

Grounded Language-Image Pre-training

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dino

PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

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ncnn

ncnn is a high-performance neural network inference framework optimized for the mobile platform

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pnnx

PyTorch Neural Network eXchange

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Adan

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

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DeepSORT

support deepsort and bytetrack MOT(Multi-object tracking) using yolov5 with C++

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pytorch-loss

label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful

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munkres-cpp

Kuhn-Munkres (Hungarian) Algorithm in C++

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FaceX-Zoo

A PyTorch Toolbox for Face Recognition

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ByteTrack

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

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yolov5

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

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onnx-simplifier

Simplify your onnx model

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CBAM.PyTorch

Non-official implement of Paper:CBAM: Convolutional Block Attention Module

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onnx-tensorrt

ONNX-TensorRT: TensorRT backend for ONNX

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torch2trt

An easy to use PyTorch to TensorRT converter

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pytorch-distributed

A quickstart and benchmark for pytorch distributed training.

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shadowsocks-windows

If you want to keep a secret, you must also hide it from yourself.

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avatarify

Avatars for Zoom and Skype

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Pytorch_Retinaface

Retinaface get 80.99% in widerface hard val using mobilenet0.25.

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automl

Google Brain AutoML

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FairMOT

A simple baseline for one-shot multi-object tracking

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MoCo

A pytorch reimplement of paper "Momentum Contrast for Unsupervised Visual Representation Learning"

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