YOLOX-RMPOLY

Related tags

Deep LearningYOLOX-RMPOLY
Overview

本算法为适应robomaster比赛，而改动自矩形识别的yolox算法。

基于旷视科技YOLOX，实现对不规则四边形的目标检测

TODO 修改onnx推理模型

更改/添加标注：

1.yolox/models/yolox_polyhead.py:
    1.1继承yolox/models/yolo_head.py YOLOXHead类，修改代码使其输出变为四点。
        1.1.1修改构造函数
        1.1.2修改get_output_and_grid函数，使其grid变为4对xy坐标的形式
        1.1.3修改forward函数
        1.1.4修改get_loses
        1.1.5把自带的l1损失函数改成smoothl1，注意它自带的是算的xywh，要改成xyxyxyxy
             正样本匹配策略还是依靠dynamic-k，用的是不规则四边形的最小外接矩形的iou

2.yolox/models/losses.py:（弃用）
新增PolyIOULoss类，iou是四边形的最小外接矩形iou，并新增四个坐标点的smoothl1_loss(弃用）

3.yolox/utils/boxes.py:
    3.1增加order_corners函数，用于给不规则四边形的四个点排序
    3.2增加minimum_outer_rect函数，用于求解四边形的最小外接矩形
    3.3增加poly_adjust_box_anns函数

4.新增exps/yolox_s_rmpoly.py配置文件
    

5.新增yolox/exp/yolox_poly_base.py配置文件基类

6.新增yolox/data/datasets/rmpoly.py
    6.1新增RMPOLYDataset类
        6.1.1修改数据集读取方式，读取八点
        6.1.2修改pull_item
        6.1.3修改load_anno

7.yolox/data/data_augment.py
    7.1新增PolyTrainTransform类，对四点数据进行数据增强（未完待续）
    7.2poly_random_affine
    7.2poly_apply_affine_to_bboxes

8.yolox/data/datasets/mosaicdetection.py
    8.1新增PolyMosaicDetection（未完待续）
    8.2_polymirror

9.yolox/models/yolox.py
    9.1 YOLOX类：
    为了适应yolox/models/yolox_polyhead.py中YOLOXPolyHead类的get_losses函数返回字典，修改forward函数中训练时返回值。（弃用）


可以试着把求解回归损失的smoothl1_loss改成归一化后的坐标再求损失。

Introduction

YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. For more details, please refer to our report on Arxiv.

This repo is an implementation of PyTorch version YOLOX, there is also a MegEngine implementation.

Updates!!

  • 【2021/08/19】 We optimize the training process with 2x faster training and ~1% higher performance! See notes for more details.
  • 【2021/08/05】 We release MegEngine version YOLOX.
  • 【2021/07/28】 We fix the fatal error of memory leak
  • 【2021/07/26】 We now support MegEngine deployment.
  • 【2021/07/20】 We have released our technical report on Arxiv.

Comming soon

  • YOLOX-P6 and larger model.
  • Objects365 pretrain.
  • Transformer modules.
  • More features in need.

Benchmark

Standard Models.

Model size mAPval
0.5:0.95		 mAPtest
0.5:0.95		 Speed V100
(ms)		 Params
(M)		 FLOPs
(G)		 weights
YOLOX-s 640 40.5 40.5 9.8 9.0 26.8 github
YOLOX-m 640 46.9 47.2 12.3 25.3 73.8 github
YOLOX-l 640 49.7 50.1 14.5 54.2 155.6 github
YOLOX-x 640 51.1 51.5 17.3 99.1 281.9 github
YOLOX-Darknet53 640 47.7 48.0 11.1 63.7 185.3 github
Legacy models
Model size mAPtest
0.5:0.95		 Speed V100
(ms)		 Params
(M)		 FLOPs
(G)		 weights
YOLOX-s 640 39.6 9.8 9.0 26.8 onedrive/github
YOLOX-m 640 46.4 12.3 25.3 73.8 onedrive/github
YOLOX-l 640 50.0 14.5 54.2 155.6 onedrive/github
YOLOX-x 640 51.2 17.3 99.1 281.9 onedrive/github
YOLOX-Darknet53 640 47.4 11.1 63.7 185.3 onedrive/github

Light Models.

Model size mAPval
0.5:0.95		 Params
(M)		 FLOPs
(G)		 weights
YOLOX-Nano 416 25.8 0.91 1.08 github
YOLOX-Tiny 416 32.8 5.06 6.45 github
Legacy models
Model size mAPval
0.5:0.95		 Params
(M)		 FLOPs
(G)		 weights
YOLOX-Nano 416 25.3 0.91 1.08 github
YOLOX-Tiny 416 32.8 5.06 6.45 github

Quick Start

Installation

Step1. Install YOLOX.

git clone [email protected]:Megvii-BaseDetection/YOLOX.git
cd YOLOX
pip3 install -U pip && pip3 install -r requirements.txt
pip3 install -v -e .  # or  python3 setup.py develop

Step2. Install pycocotools.

pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
Demo

Step1. Download a pretrained model from the benchmark table.

Step2. Use either -n or -f to specify your detector's config. For example:

python tools/demo.py image -n yolox-s -c /path/to/your/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu]

or

python tools/demo.py image -f exps/default/yolox_s.py -c /path/to/your/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu]

Demo for video:

python tools/demo.py video -n yolox-s -c /path/to/your/yolox_s.pth --path /path/to/your/video --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu]
Reproduce our results on COCO

Step1. Prepare COCO dataset

cd <YOLOX_HOME>
ln -s /path/to/your/COCO ./datasets/COCO

Step2. Reproduce our results on COCO by specifying -n:

python tools/train.py -n yolox-s -d 8 -b 64 --fp16 -o [--cache]
                         yolox-m
                         yolox-l
                         yolox-x
  • -d: number of gpu devices
  • -b: total batch size, the recommended number for -b is num-gpu * 8
  • --fp16: mixed precision training
  • --cache: caching imgs into RAM to accelarate training, which need large system RAM.

When using -f, the above commands are equivalent to:

python tools/train.py -f exps/default/yolox_s.py -d 8 -b 64 --fp16 -o [--cache]
                         exps/default/yolox_m.py
                         exps/default/yolox_l.py
                         exps/default/yolox_x.py

Multi Machine Training

We also support multi-nodes training. Just add the following args:

  • --num_machines: num of your total training nodes
  • --machine_rank: specify the rank of each node

Suppose you want to train YOLOX on 2 machines, and your master machines's IP is 123.123.123.123, use port 12312 and TCP.
On master machine, run

python tools/train.py -n yolox-s -b 128 --dist-url tcp://123.123.123.123:12312 --num-machines 2 --machine-rank 0

On the second machine, run

python tools/train.py -n yolox-s -b 128 --dist-url tcp://123.123.123.123:12312 --num-machines 2 --machine-rank 1
Evaluation

We support batch testing for fast evaluation:

python tools/eval.py -n  yolox-s -c yolox_s.pth -b 64 -d 8 --conf 0.001 [--fp16] [--fuse]
                         yolox-m
                         yolox-l
                         yolox-x
  • --fuse: fuse conv and bn
  • -d: number of GPUs used for evaluation. DEFAULT: All GPUs available will be used.
  • -b: total batch size across on all GPUs

To reproduce speed test, we use the following command:

python tools/eval.py -n  yolox-s -c yolox_s.pth -b 1 -d 1 --conf 0.001 --fp16 --fuse
                         yolox-m
                         yolox-l
                         yolox-x
Tutorials

Deployment

  1. MegEngine in C++ and Python
  2. ONNX export and an ONNXRuntime
  3. TensorRT in C++ and Python
  4. ncnn in C++ and Java
  5. OpenVINO in C++ and Python

Third-party resources

Cite YOLOX

If you use YOLOX in your research, please cite our work by using the following BibTeX entry:

 @article{yolox2021,
  title={YOLOX: Exceeding YOLO Series in 2021},
  author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
  journal={arXiv preprint arXiv:2107.08430},
  year={2021}
}
Owner
GitHub Repository
Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-art fuzzing techniques

About Fuzzification Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-

gts3.org (<a href=[email protected])"> 55 Oct 25, 2022
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zh

Payphone 8 Nov 21, 2022
Gesture Volume Control v.2

Gesture volume control v.2 In this project I am going to learn how to use Gesture Control to change the volume of a computer. I first look into hand t

Pavel Dat 23 Dec 26, 2022
Code for "Adversarial Attack Generation Empowered by Min-Max Optimization", NeurIPS 2021

Min-Max Adversarial Attacks [Paper] [arXiv] [Video] [Slide] Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang, Tianyun Zha

Jingkang Wang 12 Nov 23, 2022
Blender scripts for computing geodesic distance

GeoDoodle Geodesic distance computation for Blender meshes Table of Contents Overivew Usage Implementation Overview This addon provides an operator fo

20 Jun 08, 2022
Watch faces morph into each other with StyleGAN 2, StyleGAN, and DCGAN!

FaceMorpher FaceMorpher is an innovative project to get a unique face morph (or interpolation for geeks) on a website. Yes, this means you can see fac

Anish 9 Jun 24, 2022
Solution of Kaggle competition: Sartorius - Cell Instance Segmentation

Sartorius - Cell Instance Segmentation https://www.kaggle.com/c/sartorius-cell-instance-segmentation Environment setup Build docker image bash .dev_sc

68 Dec 09, 2022
Exploring Visual Engagement Signals for Representation Learning

Exploring Visual Engagement Signals for Representation Learning Menglin Jia, Zuxuan Wu, Austin Reiter, Claire Cardie, Serge Belongie and Ser-Nam Lim C

Menglin Jia 9 Jul 23, 2022
Global Rhythm Style Transfer Without Text Transcriptions

Global Prosody Style Transfer Without Text Transcriptions This repository provides a PyTorch implementation of AutoPST, which enables unsupervised glo

Kaizhi Qian 193 Dec 30, 2022
Code for the paper "Training GANs with Stronger Augmentations via Contrastive Discriminator" (ICLR 2021)

Training GANs with Stronger Augmentations via Contrastive Discriminator (ICLR 2021) This repository contains the code for reproducing the paper: Train

Jongheon Jeong 174 Dec 29, 2022
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting This is the origin Pytorch implementation of Informer in the followin

Haoyi 3.1k Dec 29, 2022
Official pytorch implementation of the IrwGAN for unaligned image-to-image translation

IrwGAN (ICCV2021) Unaligned Image-to-Image Translation by Learning to Reweight [Update] 12/15/2021 All dataset are released, trained models and genera

37 Nov 09, 2022
Scrutinizing XAI with linear ground-truth data

This repository contains all the experiments presented in the corresponding paper: "Scrutinizing XAI using linear ground-truth data with suppressor va

braindata lab 2 Oct 04, 2022
Implementation of our paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"

DMT: Dynamic Mutual Training for Semi-Supervised Learning This repository contains the code for our paper DMT: Dynamic Mutual Training for Semi-Superv

Zhengyang Feng 120 Dec 30, 2022
Eye-Blink-Counter - Python based Computer Vision project which counts how many time a person blinks

Eye Blink Counter OpenCV and Mediapipe No Blink Blink

Rohit Ingole 2 Mar 24, 2022
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer

BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer Project Page | Paper | Video State-of-the-art image-to-image translatio

47 Dec 06, 2022
Code for paper Novel View Synthesis via Depth-guided Skip Connections

Novel View Synthesis via Depth-guided Skip Connections Code for paper Novel View Synthesis via Depth-guided Skip Connections @InProceedings{Hou_2021_W

8 Mar 14, 2022
Learning trajectory representations using self-supervision and programmatic supervision.

Trajectory Embedding for Behavior Analysis (TREBA) Implementation from the paper: Jennifer J. Sun, Ann Kennedy, Eric Zhan, David J. Anderson, Yisong Y

58 Jan 06, 2023
Modular Probabilistic Programming on MXNet

MXFusion | | | | Tutorials | Documentation | Contribution Guide MXFusion is a modular deep probabilistic programming library. With MXFusion Modules yo

Amazon 100 Dec 10, 2022
Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"

Optimization as a Model for Few-Shot Learning This repo provides a Pytorch implementation for the Optimization as a Model for Few-Shot Learning paper.

Albert Berenguel Centeno 238 Jan 04, 2023