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