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Yu JX's Projects

faster-rcnn-pytorch icon faster-rcnn-pytorch

这是一个faster-rcnn的pytorch实现的库,可以利用voc数据集格式的数据进行训练。

fpga_tutorial icon fpga_tutorial

帮助大家进行FPGA的入门,分享FPGA相关的优秀文章,优秀项目

fsdetview icon fsdetview

(ECCV 2020) PyTorch implementation of paper "Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild"

fsl-mate icon fsl-mate

FSL-Mate: A collection of resources for few-shot learning (FSL).

fsodm icon fsodm

Official Code for paper "Few-shot Object Detection on Remote Sensing Images"

ha1588 icon ha1588

Hardware Assisted IEEE 1588 IP Core

meta-detr icon meta-detr

[T-PAMI 2022] Meta-DETR for Few-Shot Object Detection: Official PyTorch Implementation

meta-faster-r-cnn icon meta-faster-r-cnn

Code for AAAI 2022 Oral paper: 'Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment'

meta-rcnn icon meta-rcnn

Meta R-CNN : Towards General Solver for Instance-level Low-shot Learning

meta-yolo icon meta-yolo

Few-shot Object Detection via Feature Reweighting

micronet icon micronet

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

mmfewshot icon mmfewshot

OpenMMLab FewShot Learning Toolbox and Benchmark

mmselfsup icon mmselfsup

OpenMMLab Self-Supervised Learning Toolbox and Benchmark

msocl icon msocl

The code for “Multiscale Object Contrastive Learning–Derived Few-Shot Object Detection in VHR imagery”

netron icon netron

Visualizer for neural network, deep learning, and machine learning models

nni icon nni

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

oan icon oan

针对ORSI中目标局部密集/广域稀疏的分布特点,提取存在潜在目标区域的方法

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