Blendmask-sourceCode源码+内含如何进行具体安装环境与部署

<div align="center"> <img src="docs/adel-logo.svg" width="160" > </div> # AdelaiDet AdelaiDet is an open source toolbox for multiple instance-level recognition tasks on top of [Detectron2](https://github.com/facebookresearch/detectron2). All instance-level recognition works from our group are open-sourced here. To date, AdelaiDet implements the following algorithms: * [FCOS](configs/FCOS-Detection/README.md) * [BlendMask](configs/BlendMask/README.md) * [MEInst](configs/MEInst-InstanceSegmentation/README.md) * [ABCNet](configs/BAText/README.md) * [ABCNetv2](configs/BAText#quick-start-abcnetv2) * [CondInst](configs/CondInst/README.md) * [SOLO](https://arxiv.org/abs/1912.04488) ([mmdet version](https://github.com/WXinlong/SOLO)) * [SOLOv2](configs/SOLOv2/README.md) * [BoxInst](configs/BoxInst/README.md) ([video demo](https://www.youtube.com/watch?v=NuF8NAYf5L8)) * [DenseCL](configs/DenseCL/README.md) * [FCPose](configs/FCPose/README.md) * [DirectPose](https://arxiv.org/abs/1911.07451) _to be released_ ## Models ### COCO Object Detecton Baselines with [FCOS](https://arxiv.org/abs/1904.01355) Name | inf. time | box AP | download --- |:---:|:---:|:--- [FCOS_R_50_1x](configs/FCOS-Detection/R_50_1x.yaml) | 16 FPS | 38.7 | [model](https://cloudstor.aarnet.edu.au/plus/s/glqFc13cCoEyHYy/download) [FCOS_MS_R_101_2x](configs/FCOS-Detection/MS_R_101_2x.yaml) | 12 FPS | 43.1 | [model](https://cloudstor.aarnet.edu.au/plus/s/M3UOT6JcyHy2QW1/download) [FCOS_MS_X_101_32x8d_2x](configs/FCOS-Detection/MS_X_101_32x8d_2x.yaml) | 6.6 FPS | 43.9 | [model](https://cloudstor.aarnet.edu.au/plus/s/R7H00WeWKZG45pP/download) [FCOS_MS_X_101_32x8d_dcnv2_2x](configs/FCOS-Detection/MS_X_101_32x8d_2x_dcnv2.yaml) | 4.6 FPS | 46.6 | [model](https://cloudstor.aarnet.edu.au/plus/s/TDsnYK8OXDTrafF/download) [FCOS_RT_MS_DLA_34_4x_shtw](configs/FCOS-Detection/FCOS_RT/MS_DLA_34_4x_syncbn_shared_towers.yaml) | 52 FPS | 39.1 | [model](https://cloudstor.aarnet.edu.au/plus/s/4vc3XwQezyhNvnB/download) More models can be found in FCOS [README.md](configs/FCOS-Detection/README.md). ### COCO Instance Segmentation Baselines with [BlendMask](https://arxiv.org/abs/2001.00309) Model | Name |inf. time | box AP | mask AP | download --- |:---:|:---:|:---:|:---:|:---: Mask R-CNN | [R_101_3x](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml) | 10 FPS | 42.9 | 38.6 | BlendMask | [R_101_3x](configs/BlendMask/R_101_3x.yaml) | 11 FPS | 44.8 | 39.5 | [model](https://cloudstor.aarnet.edu.au/plus/s/e4fXrliAcMtyEBy/download) BlendMask | [R_101_dcni3_5x](configs/BlendMask/R_101_dcni3_5x.yaml) | 10 FPS | 46.8 | 41.1 | [model](https://cloudstor.aarnet.edu.au/plus/s/vbnKnQtaGlw8TKv/download) For more models and information, please refer to BlendMask [README.md](configs/BlendMask/README.md). ### COCO Instance Segmentation Baselines with [MEInst](https://arxiv.org/abs/2003.11712) Name | inf. time | box AP | mask AP | download --- |:---:|:---:|:---:|:---: [MEInst_R_50_3x](https://github.com/aim-uofa/AdelaiDet/configs/MEInst-InstanceSegmentation/MEInst_R_50_3x.yaml) | 12 FPS | 43.6 | 34.5 | [model](https://cloudstor.aarnet.edu.au/plus/s/1ID0DeuI9JsFQoG/download) For more models and information, please refer to MEInst [README.md](configs/MEInst-InstanceSegmentation/README.md). ### Total_Text results with [ABCNet](configs/BAText/README.md) Name | inf. time | e2e-hmean | det-hmean | download --- |:---------:|:---------:|:---------:|:---: [v1-totaltext](configs/BAText/TotalText/attn_R_50.yaml) | 11 FPS | 67.1 | 86.0 | [model](https://cloudstor.aarnet.edu.au/plus/s/t2EFYGxNpKPUqhc/download) [v2-totaltext](configs/BAText/TotalText/v2_attn_R_50.yaml) | 7.7 FPS | 71.8 | 87.2 | [model](https://drive.google.com/file/d/1jR5-A-7ITvjdSx3kWVE9bMgh_biMsqcR/view?usp=sharing) For more models and information, please refer to ABCNet [README.md](configs/BAText/README.md). ### COCO Instance Segmentation Baselines with [CondInst](https://arxiv.org/abs/2003.05664) Name | inf. time | box AP | mask AP | download --- |:---:|:---:|:---:|:---: [CondInst_MS_R_50_1x](configs/CondInst/MS_R_50_1x.yaml) | 14 FPS | 39.7 | 35.7 | [model](https://cloudstor.aarnet.edu.au/plus/s/Trx1r4tLJja7sLT/download) [CondInst_MS_R_50_BiFPN_3x_sem](configs/CondInst/MS_R_50_BiFPN_3x_sem.yaml) | 13 FPS | 44.7 | 39.4 | [model](https://cloudstor.aarnet.edu.au/plus/s/9cAHjZtdaAGnb2Q/download) [CondInst_MS_R_101_3x](configs/CondInst/MS_R_101_3x.yaml) | 11 FPS | 43.3 | 38.6 | [model](https://cloudstor.aarnet.edu.au/plus/s/vWLiYm8OnrTSUD2/download) [CondInst_MS_R_101_BiFPN_3x_sem](configs/CondInst/MS_R_101_BiFPN_3x_sem.yaml) | 10 FPS | 45.7 | 40.2 | [model](https://cloudstor.aarnet.edu.au/plus/s/2p1ashxl54Su8vv/download) For more models and information, please refer to CondInst [README.md](configs/CondInst/README.md). Note that: - Inference time for all projects is measured on a NVIDIA 1080Ti with batch size 1. - APs are evaluated on COCO2017 val split unless specified. ## Installation First install Detectron2 following the official guide: [INSTALL.md](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md). *Please use Detectron2 with commit id [9eb4831](https://github.com/facebookresearch/detectron2/commit/9eb4831f742ae6a13b8edb61d07b619392fb6543) if you have any issues related to Detectron2.* Then build AdelaiDet with: ``` git clone https://github.com/aim-uofa/AdelaiDet.git cd AdelaiDet python setup.py build develop ``` If you are using docker, a pre-built image can be pulled with: ``` docker pull tianzhi0549/adet:latest ``` Some projects may require special setup, please follow their own `README.md` in [configs](configs). ## Quick Start ### Inference with Pre-trained Models 1. Pick a model and its config file, for example, `fcos_R_50_1x.yaml`. 2. Download the model `wget https://cloudstor.aarnet.edu.au/plus/s/glqFc13cCoEyHYy/download -O fcos_R_50_1x.pth` 3. Run the demo with ``` python demo/demo.py \ --config-file configs/FCOS-Detection/R_50_1x.yaml \ --input input1.jpg input2.jpg \ --opts MODEL.WEIGHTS fcos_R_50_1x.pth ``` ### Train Your Own Models To train a model with "train_net.py", first setup the corresponding datasets following [datasets/README.md](https://github.com/facebookresearch/detectron2/blob/master/datasets/README.md), then run: ``` OMP_NUM_THREADS=1 python tools/train_net.py \ --config-file configs/FCOS-Detection/R_50_1x.yaml \ --num-gpus 8 \ OUTPUT_DIR training_dir/fcos_R_50_1x ``` To evaluate the model after training, run: ``` OMP_NUM_THREADS=1 python tools/train_net.py \ --config-file configs/FCOS-Detection/R_50_1x.yaml \ --eval-only \ --num-gpus 8 \ OUTPUT_DIR training_dir/fcos_R_50_1x \ MODEL.WEIGHTS training_dir/fcos_R_50_1x/model_final.pth ``` Note that: - The configs are made for 8-GPU training. To train on another number of GPUs, change the `--num-gpus`. - If you want to measure the inference time, please change `--num-gpus` to 1. - We set `OMP_NUM_THREADS=1` by default, which achieves the best speed on our machines, please change it as needed. - This quick start is made for FCOS. If you are using other projects, please check the projects' own `README.md` in [configs](configs). ## Acknowledgements The authors are grateful to Nvidia, Huawei Noah's Ark Lab, ByteDance, Adobe who generously donated GPU computing in the past a few years. ## Citing AdelaiDet If you use this toolbox in your research or wish to refer to the baseline results published here, please use the following BibTeX entries: ```BibTeX @misc{tian2019adelaidet, author = {Tian, Zhi and Chen, Hao and Wang, Xinlong and Liu, Yuliang and Shen, Chunhua}, title = {{AdelaiDet}: A Toolbox for Instance-level Recognition Tasks}, howpublished = {\url{https://git.io/adelaidet}}, year = {2019} } ``` and relevant publications: ```BibTeX @inproceedings{tian2019fcos, title = {{FCOS}: Fully Con