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
Deployment
- MegEngine in C++ and Python
- ONNX export and an ONNXRuntime
- TensorRT in C++ and Python
- ncnn in C++ and Java
- OpenVINO in C++ and Python
Third-party resources
- The ncnn android app with video support: ncnn-android-yolox from FeiGeChuanShu
- YOLOX with Tengine support: Tengine from BUG1989
- YOLOX + ROS2 Foxy: YOLOX-ROS from Ar-Ray
- YOLOX Deploy DeepStream: YOLOX-deepstream from nanmi
- YOLOX MNN/TNN/ONNXRuntime: YOLOX-MNN、YOLOX-TNN and YOLOX-ONNXRuntime C++ from DefTruth
- Converting darknet or yolov5 datasets to COCO format for YOLOX: YOLO2COCO from Daniel
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}
}
33 Dec 28, 2022
5 Jun 8, 2022
9 Aug 22, 2022
6 Dec 18, 2022
16 Nov 18, 2022
12 Nov 9, 2022
7.7k Jan 3, 2023
245 Dec 26, 2022
77 Nov 22, 2022
158 Dec 21, 2022
21 Dec 28, 2022
91 Dec 26, 2022
18 Aug 18, 2022
148 Jan 3, 2023
16 Nov 18, 2022
12 Nov 9, 2022
7.7k Jan 3, 2023
245 Dec 26, 2022
77 Nov 22, 2022
158 Dec 21, 2022
21 Dec 28, 2022
91 Dec 26, 2022
18 Aug 18, 2022
148 Jan 3, 2023