CV之detectron2：detectron2的简介、安装、使用方法之详细攻略
CV之detectron2：detectron2的簡介、安裝、使用方法之詳細(xì)攻略
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目錄
detectron2的簡介
1、Detectron2—What's New
detectron2的安裝
1、Requirements
2、Build and Install Detectron2
1、官方安裝
2、Windows下安裝
3、Detectron2 Model Zoo and Baselines
COCO Object Detection Baselines
COCO Instance Segmentation Baselines with Mask R-CNN
COCO Person Keypoint Detection Baselines with Keypoint R-CNN
COCO Panoptic Segmentation Baselines with Panoptic FPN
LVIS Instance Segmentation Baselines with Mask R-CNN
Cityscapes & Pascal VOC Baselines
Other Settings
detectron2的使用方法
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detectron2的簡介
| ? ? ? ? ?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人工智能研究的軟件系統(tǒng)，它實(shí)現(xiàn)了最先進(jìn)的目標(biāo)檢測算法，包括Mask R-CNN。它是用Python編寫的，由Caffe2深度學(xué)習(xí)框架提供支持。 ? ? ? ? ?在Facebook人工智能研究中，Detectron已經(jīng)啟動了許多研究項(xiàng)目，包括：用于物體檢測的特征金字塔網(wǎng)絡(luò)、掩模R-CNN、檢測和識別人類與物體的相互作用、用于密集物體檢測的焦距損失、非局部神經(jīng)網(wǎng)絡(luò)、學(xué)習(xí)分割每件事物、數(shù)據(jù)蒸餾：朝向全監(jiān)督學(xué)習(xí)，DensePose：在野外進(jìn)行密集的人體姿勢估計，并進(jìn)行組規(guī)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軟件系統(tǒng)，實(shí)現(xiàn)了最先進(jìn)的目標(biāo)檢測算法。它是對先前版本Detectron的一次徹底重寫，它源于maskrcnn基準(zhǔn)測試。 |
GitHub：
Detectron，https://github.com/facebookresearch/Detectron/
Detectron2，https://github.com/facebookresearch/detectron2
? ? ? ?Detectron的目的是為目標(biāo)檢測研究提供高質(zhì)量、高性能的codebase。它的設(shè)計是靈活的，以支持快速實(shí)施和評估的新研究。Detectron包括以下對象檢測算法的實(shí)現(xiàn)：
- 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
? ? ? ?采用下列主干網(wǎng)絡(luò)架構(gòu):
- ResNeXt{50,101,152}
- ResNet{50,101,152}
- Feature Pyramid Networks?(with ResNet/ResNeXt)
- VGG16
? ? ? ?附加的主干架構(gòu)可能很容易實(shí)現(xiàn)。有關(guān)這些模型的詳細(xì)信息，請參閱下面的參考資料。
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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.
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detectron2的安裝
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、官方安裝
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下安裝
git clone https://github.com/facebookresearch/detectron2.git cd detectron2 python setup.py build develop相關(guān)文章：CV之detectron2：detectron2安裝過程記錄
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3、Detectron2 Model Zoo and Baselines
COCO Object Detection Baselines
Faster R-CNN:
| 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:
| 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:
| 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
| 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
| 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
| 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.
| 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)
| 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:
| 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)
| 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:
| 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 | ? | ? |
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detectron2的使用方法
1、demo測試
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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