Code base of object detection

Last update: Mar 08, 2022

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Overview

rmdet

code base of object detection.

环境安装：

1. 安装conda python环境   
- `conda create -n xxx python=3.7/3.8`  
- `conda activate xxx`
2. 运行脚本，自动安装pytorch1.8.0环境
- `bash install.sh'

训练：

1. 修改`configs`里的模型配置文件，包括数据集路径，类别等；
2. 运行`train.py`训练，设置config，epochs，gpu等：
- `python train.py -c configs/xxx.yaml --epochs * --n_gpu *`

测试：

`python val.py -c configs/xxx.yaml --weights (训练完成的模型权重)`

推理：

`python detect.py -c configs/xxx.yaml --weights (权重) -s (voc/cam)`

Reference:

YOLOv5: https://github.com/ultralytics/yolov5
MMDetection:https://github.com/open-mmlab/mmdetection
DBB: https://github.com/DingXiaoH/DiverseBranchBlock
RepVGG:https://github.com/DingXiaoH/RepVGG
RetinaNet:https://github.com/zhenghao977/RetinaNet-Pytorch-36.4AP

Owner

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Code base of object detection

Related tags

Overview

rmdet

环境安装：

训练：

测试：

推理：

Reference:

Owner

An Unpaired Sketch-to-Photo Translation Model

PyTorch implementation of Towards Accurate Alignment in Real-time 3D Hand-Mesh Reconstruction (ICCV 2021).

Code for the ICME 2021 paper "Exploring Driving-Aware Salient Object Detection via Knowledge Transfer"

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Codes for AAAI 2022 paper: Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

Implementation of Invariant Point Attention, used for coordinate refinement in the structure module of Alphafold2, as a standalone Pytorch module

Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

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PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in clustering (CVPR2021)

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