detectron2的简介

Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.

At FAIR, Detectron has enabled numerous research projects, including: Feature Pyramid Networks for Object Detection, Mask R-CNN, Detecting and Recognizing Human-Object Interactions, Focal Loss for Dense Object Detection, Non-local Neural Networks, Learning to Segment Every Thing, Data Distillation: Towards Omni-Supervised Learning, DensePose: Dense Human Pose Estimation In The Wild, and Group Normalization. Detectron是Facebook人工智能研究的软件系统，它实现了最先进的目标检测算法，包括Mask R-CNN。它是用Python编写的，由Caffe2深度学习框架提供支持。

在Facebook人工智能研究中，Detectron已经启动了许多研究项目，包括：用于物体检测的特征金字塔网络、掩模R-CNN、检测和识别人类与物体的相互作用、用于密集物体检测的焦距损失、非局部神经网络、学习分割每件事物、数据蒸馏：朝向全监督学习，DensePose：在野外进行密集的人体姿势估计，并进行组规范化。

Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. Detectron2是Facebook人工智能研究的下一代软件系统，实现了最先进的目标检测算法。它是对先前版本Detectron的一次彻底重写，它源于maskrcnn基准测试。

GitHub：

Detectron，https://github.com/facebookresearch/Detectron/

Detectron2，https://github.com/facebookresearch/detectron2

Detectron的目的是为目标检测研究提供高质量、高性能的codebase。它的设计是灵活的，以支持快速实施和评估的新研究。Detectron包括以下对象检测算法的实现：

Mask R-CNN -- Marr Prize at ICCV 2017

RetinaNet -- Best Student Paper Award at ICCV 2017

Faster R-CNN

RPN

Fast R-CNN

R-FCN

采用下列主干网络架构:

ResNeXt{50,101,152}

ResNet{50,101,152}

Feature Pyramid Networks (with ResNet/ResNeXt)

VGG16

附加的主干架构可能很容易实现。有关这些模型的详细信息，请参阅下面的参考资料。

1、Detectron2—What's New

It is powered by the PyTorch deep learning framework.

Includes more features such as panoptic segmentation, densepose, Cascade R-CNN, rotated bounding boxes, etc.

Can be used as a library to support different projects on top of it. We'll open source more research projects in this way.

It trains much faster.

detectron2的安装

1、Requirements

Linux or macOS

Python ≥ 3.6

PyTorch ≥ 1.3

torchvision that matches the PyTorch installation. You can install them together at pytorch.org to make sure of this.

OpenCV, needed by demo and visualization

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

GCC ≥ 4.9

2、Build and Install Detectron2

1、官方安装

git clone https://github.com/facebookresearch/detectron2.git

cd detectron2

pip install -e .

# (add --user if you don't have permission)

# or if you are on macOS

# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ pip install -e .

2、Windows下安装

git clone https://github.com/facebookresearch/detectron2.git

cd detectron2

python setup.py build develop

相关文章：CV之detectron2：detectron2安装过程记录

3、Detectron2 Model Zoo and Baselines

COCO Object Detection Baselines

Faster R-CNN:

Name lr

sched train

time

(s/iter) inference

time

(s/im) train

mem

(GB) box

AP model id download

R50-C4 1x 0.551 0.110 4.8 35.7 137257644 model | metrics

R50-DC5 1x 0.380 0.068 5.0 37.3 137847829 model | metrics

R50-FPN 1x 0.210 0.055 3.0 37.9 137257794 model | metrics

R50-C4 3x 0.543 0.110 4.8 38.4 137849393 model | metrics

R50-DC5 3x 0.378 0.073 5.0 39.0 137849425 model | metrics

R50-FPN 3x 0.209 0.047 3.0 40.2 137849458 model | metrics

R101-C4 3x 0.619 0.149 5.9 41.1 138204752 model | metrics

R101-DC5 3x 0.452 0.082 6.1 40.6 138204841 model | metrics

R101-FPN 3x 0.286 0.063 4.1 42.0 137851257 model | metrics

X101-FPN 3x 0.638 0.120 6.7 43.0 139173657 model | metrics

RetinaNet:

Name lr

sched train

time

(s/iter) inference

time

(s/im) train

mem

(GB) box

AP model id download

R50 1x 0.200 0.062 3.9 36.5 137593951 model | metrics

R50 3x 0.201 0.063 3.9 37.9 137849486 model | metrics

R101 3x 0.280 0.080 5.1 39.9 138363263 model | metrics

RPN & Fast R-CNN:

Name lr

sched train

time

(s/iter) inference

time

(s/im) train

mem

(GB) box

AP prop.

AR model id download

RPN R50-C4 1x 0.130 0.051 1.5 51.6 137258005 model | metrics

RPN R50-FPN 1x 0.186 0.045 2.7 58.0 137258492 model | metrics

Fast R-CNN R50-FPN 1x 0.140 0.035 2.6 37.8 137635226 model | metrics

COCO Instance Segmentation Baselines with Mask R-CNN

Name lr

sched train

time

(s/iter) inference

time

(s/im) train

mem

(GB) box

AP mask

AP model id download

R50-C4 1x 0.584 0.117 5.2 36.8 32.2 137259246 model | metrics

R50-DC5 1x 0.471 0.074 6.5 38.3 34.2 137260150 model | metrics

R50-FPN 1x 0.261 0.053 3.4 38.6 35.2 137260431 model | metrics

R50-C4 3x 0.575 0.118 5.2 39.8 34.4 137849525 model | metrics

R50-DC5 3x 0.470 0.075 6.5 40.0 35.9 137849551 model | metrics

R50-FPN 3x 0.261 0.055 3.4 41.0 37.2 137849600 model | metrics

R101-C4 3x 0.652 0.155 6.3 42.6 36.7 138363239 model | metrics

R101-DC5 3x 0.545 0.155 7.6 41.9 37.3 138363294 model | metrics

R101-FPN 3x 0.340 0.070 4.6 42.9 38.6 138205316 model | metrics

X101-FPN 3x 0.690 0.129 7.2 44.3 39.5 139653917 model | metrics

COCO Person Keypoint Detection Baselines with Keypoint R-CNN

Name lr

sched train

time

(s/iter) inference

time

(s/im) train

mem

(GB) box

AP kp.

AP model id download

R50-FPN 1x 0.315 0.083 5.0 53.6 64.0 137261548 model | metrics

R50-FPN 3x 0.316 0.076 5.0 55.4 65.5 137849621 model | metrics

R101-FPN 3x 0.390 0.090 6.1 56.4 66.1 138363331 model | metrics

X101-FPN 3x 0.738 0.142 8.7 57.3 66.0 139686956 model | metrics

COCO Panoptic Segmentation Baselines with Panoptic FPN

Name lr

sched train

time

(s/iter) inference

time

(s/im) train

mem

(GB) box

AP mask

AP PQ model id download

R50-FPN 1x 0.304 0.063 4.8 37.6 34.7 39.4 139514544 model | metrics

R50-FPN 3x 0.302 0.063 4.8 40.0 36.5 41.5 139514569 model | metrics

R101-FPN 3x 0.392 0.078 6.0 42.4 38.5 43.0 139514519 model | metrics

LVIS Instance Segmentation Baselines with Mask R-CNN

Mask R-CNN baselines on the LVIS dataset, v0.5. These baselines are described in Table 3(c) of the LVIS paper.

NOTE: the 1x schedule here has the same amount of iterations as the COCO 1x baselines. They are roughly 24 epochs of LVISv0.5 data. The final results of these configs have large variance across different runs.

Name lr

sched train

time

(s/iter) inference

time

(s/im) train

mem

(GB) box

AP mask

AP model id download

R50-FPN 1x 0.292 0.127 7.1 23.6 24.4 144219072 model | metrics

R101-FPN 1x 0.371 0.124 7.8 25.6 25.9 144219035 model | metrics

X101-FPN 1x 0.712 0.166 10.2 26.7 27.1 144219108 model | metrics

Cityscapes & Pascal VOC Baselines

Simple baselines for

Mask R-CNN on Cityscapes instance segmentation (initialized from COCO pre-training, then trained on Cityscapes fine annotations only)

Faster R-CNN on PASCAL VOC object detection (trained on VOC 2007 train+val + VOC 2012 train+val, tested on VOC 2007 using 11-point interpolated AP)

Name train

time

(s/iter) inference

time

(s/im) train

mem

(GB) box

AP box

AP50 mask

AP model id download

R50-FPN, Cityscapes 0.240 0.092 4.4 36.5 142423278 model | metrics

R50-C4, VOC 0.537 0.086 4.8 51.9 80.3 142202221 model | metrics

Other Settings

Ablations for Deformable Conv and Cascade R-CNN:

Name lr

sched train

time

(s/iter) inference

time

(s/im) train

mem

(GB) box

AP mask

AP model id download

Baseline R50-FPN 1x 0.261 0.053 3.4 38.6 35.2 137260431 model | metrics

Deformable Conv 1x 0.342 0.061 3.5 41.5 37.5 138602867 model | metrics

Cascade R-CNN 1x 0.317 0.066 4.0 42.1 36.4 138602847 model | metrics

Baseline R50-FPN 3x 0.261 0.055 3.4 41.0 37.2 137849600 model | metrics

Deformable Conv 3x 0.349 0.066 3.5 42.7 38.5 144998336 model | metrics

Cascade R-CNN 3x 0.328 0.075 4.0 44.3 38.5 144998488 model | metrics

Ablations for normalization methods: (Note: The baseline uses 2fc head while the others use 4conv1fc head. According to the GroupNorm paper, the change in head does not improve the baseline by much)

Name lr

sched train

time

(s/iter) inference

time

(s/im) train

mem

(GB) box

AP mask

AP model id download

Baseline R50-FPN 3x 0.261 0.055 3.4 41.0 37.2 137849600 model | metrics

SyncBN 3x 0.464 0.063 5.6 42.0 37.8 143915318 model | metrics

GN 3x 0.356 0.077 7.3 42.6 38.6 138602888 model | metrics

GN (scratch) 3x 0.400 0.077 9.8 39.9 36.6 138602908 model | metrics

A few very large models trained for a long time, for demo purposes:

Name inference

time

(s/im) train

mem

(GB) box

AP mask

AP PQ model id download

Panoptic FPN R101 0.123 11.4 47.4 41.3 46.1 139797668 model | metrics

Mask R-CNN X152 0.281 15.1 50.2 44.0 18131413 model | metrics

above + test-time aug. 51.9 45.9

detectron2的使用方法

1、demo测试

python demo/demo.py --config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input input1.jpg input2.jpg [--other-options] --opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl

————————————————

原文链接：https://blog.csdn.net/qq_41185868/article/details/103151022