Kaijun Liu's repositories
PaddleViT
:robot: PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+
ECNU-Dissertations-Latex-Template
华东师范大学硕(博)士论文LaTex模板
Yolo-to-COCO-format-converter
Yolo to COCO annotation format converter
detr
End-to-End Object Detection with Transformers
mobilenet-ssd-keras
这是一个mobilenet-ssd-keras的源码,可以用于训练自己的轻量级ssd模型。
Mobilenet-SSD-Essay
这是Mobilenet-SSD的论文版,可用于训练与预测。
OneNet
OneNet: End-to-End One-Stage Object Detection
centerX
This repo is implemented based on detectron2 and centernet
nanodet
⚡Super fast and lightweight anchor-free object detection model. 🔥Only 1.8mb and run 97FPS on cellphone🔥
yolov5
YOLOv5 in PyTorch > ONNX > CoreML > TFLite
few-shot-object-detection
Implementations of few-shot object detection benchmarks
few-shot-classification-leaderboard
Leaderboards for few-shot image classification on miniImageNet, tieredImageNet, FC100, and CIFAR-FS.
YOLO-v5
:art: Pytorch YOLO v5 训练自己的数据集超详细教程!!! :art: (提供PDF训练教程下载)
Unsupervised-Classification
SCAN: Learning to Classify Images without Labels (ECCV 2020)
negative-margin.few-shot
PyTorch implementation of “Negative Margin Matters: Understanding Margin in Few-shot Classification”
Awesome-Zero-Shot-Learning
A paper collection of brand-new ZSL methods, according to different algorithm.
PyTorchTricks
Some tricks of pytorch... :star:
FSS-1000
FSS-1000, A 1000-class Dataset For Few-shot Segmentation
learning-to-self-train
Learning to Self-Train for Semi-Supervised Few-Shot
Few-Shot-Semantic-Segmentation-Papers
Few Shot Semantic Segmentation Papers
d2l-zh
《动手学深度学习》:面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。
mask-rcnn-keras
这是一个mask-rcnn的库,可以用于训练自己的实例分割模型。
Journal-Information
The information of computer journal
u2net_torch
MICCAI2019:3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation
MICCAI-OpenSourcePapers
MICCAI 2019 Open Source Papers
inc-few-shot-attractor-public
Code for Paper "Incremental Few-Shot Learning with Attention Attractor Networks"
DeepEMD
Code for paper "DeepEMD: Few-Shot Image Classification with Differentiable Earth Mover's Distance and Structured Classifiers", CVPR2020
Detection-PyTorch-Notebook
代码 -《深度学习之PyTorch物体检测实战》