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lmhlll's Projects

hrnet-object-detection icon hrnet-object-detection

Object detection with multi-level representations generated from deep high-resolution representation learning (HRNetV2h).

hyperlandmark icon hyperlandmark

Deep Learning Based Free Mobile Real-Time Face Landmark Localization.

kmeans-anchor-boxes icon kmeans-anchor-boxes

k-means clustering with the Intersection over Union (IoU) metric as described in the YOLO9000 paper

lab icon lab

[CVPR 2018] Look at Boundary: A Boundary-Aware Face Alignment Algorithm

learningtocompare_zsl icon learningtocompare_zsl

PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Zero-Shot Learning part)

leetcodeanimation icon leetcodeanimation

Demonstrate all the questions on LeetCode in the form of animation.(用动画的形式呈现解LeetCode题目的思路)

leetcodebook icon leetcodebook

leetcode 1~400知识点&题型总结&leetcode对应题表

mae icon mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

mmfewshot icon mmfewshot

OpenMMLab FewShot Learning Toolbox and Benchmark

pix2pix icon pix2pix

Image-to-image translation with conditional adversarial nets

pose-hg-train icon pose-hg-train

Training and experimentation code used for "Stacked Hourglass Networks for Human Pose Estimation"

ros_yolov3 icon ros_yolov3

在自定义数据集上训练yolov3,并封装到ROS中作为一个节点

scan2cap icon scan2cap

[CVPR 2021] Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

simpledet icon simpledet

A Simple and Versatile Framework for Object Detection and Instance Recognition

ssfcn icon ssfcn

Spectral-Spatial Fully Convolutional Networks for Hyperspectral Image Classification

tensorflow-mtcnn icon tensorflow-mtcnn

人脸检测MTCNN算法,采用tensorflow框架编写,从理解到训练,中文注释完全,含测试和训练,支持摄像头

vpt icon vpt

Unofficial code for VPT(Visual Prompt Tuning) paper of arxiv 2203.12119

vqa-counting icon vqa-counting

[ICLR 2018] Learning to Count Objects in Natural Images for Visual Question Answering

vqa2.0-recent-approachs-2018.pytorch icon vqa2.0-recent-approachs-2018.pytorch

A pytroch reimplementation of "Bilinear Attention Network", "Intra- and Inter-modality Attention", "Learning Conditioned Graph Structures", "Learning to count object", "Bottom-up top-down" for Visual Question Answering 2.0

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