基于python开发的工业场景的脱轨检测.+源码（毕业设计&课程设计&项目开发）

# SOLOv2: Dynamic and Fast Instance Segmentation > [**SOLOv2: Dynamic and Fast Instance Segmentation**](https://arxiv.org/abs/2003.10152), > Xinlong Wang, Rufeng Zhang, Tao Kong, Lei Li, Chunhua Shen > In: Proc. Advances in Neural Information Processing Systems (NeurIPS), 2020 > *arXiv preprint ([arXiv 2003.10152](https://arxiv.org/abs/2003.10152))* # Installation & Quick Start First, follow the [default instruction](../../README.md#Installation) to install the project and [datasets/README.md](https://github.com/facebookresearch/detectron2/blob/master/datasets/README.md) set up the datasets (e.g., MS-COCO). For demo, run the following command lines: ``` wget https://cloudstor.aarnet.edu.au/plus/s/chF3VKQT4RDoEqC/download -O SOLOv2_R50_3x.pth python demo/demo.py \ --config-file configs/SOLOv2/R50_3x.yaml \ --input input1.jpg input2.jpg \ --opts MODEL.WEIGHTS SOLOv2_R50_3x.pth ``` For training on COCO, run: ``` OMP_NUM_THREADS=1 python tools/train_net.py \ --config-file configs/SOLOv2/R50_3x.yaml \ --num-gpus 8 \ OUTPUT_DIR training_dir/SOLOv2_R50_3x ``` For evaluation on COCO, run: ``` OMP_NUM_THREADS=1 python tools/train_net.py \ --config-file configs/SOLOv2/R50_3x.yaml \ --eval-only \ --num-gpus 8 \ OUTPUT_DIR training_dir/SOLOv2_R50_3x \ MODEL.WEIGHTS training_dir/SOLOv2_R50_3x/model_final.pth ``` ## Models ### COCO Instance Segmentation Baselines with SOLOv2 Name | inf. time | train. time | Mem | box AP | mask AP | download --- |:---:|:---:|:---:|:---:|:---:|:---: [SOLOv2_R50_3x](R50_3x.yaml) | 47ms | ~25h(36 epochs) | 3.7GB | - | 37.6 | [model](https://cloudstor.aarnet.edu.au/plus/s/chF3VKQT4RDoEqC/download) [SOLOv2_R101_3x](R101_3x.yaml) | 61ms | ~30h(36 epochs) | 4.7GB | - | 39.0 | [model](https://cloudstor.aarnet.edu.au/plus/s/9w7b3sjaXvqYQEQ) *Disclaimer:* - All models are trained with multi-scale data augmentation. - Inference time is measured on a single V100 GPU. Training time is measured on 8 V100 GPUs. - This is a reimplementation. Thus, the numbers are slightly different from our original paper (within 0.3% in mask AP). - The implementation on mmdetection is available at [https://github.com/WXinlong/SOLO](https://github.com/WXinlong/SOLO). # Citations Please consider citing our papers in your publications if the project helps your research. BibTeX reference is as follows. ```BibTeX @inproceedings{wang2020solo, title = {{SOLO}: Segmenting Objects by Locations}, author = {Wang, Xinlong and Kong, Tao and Shen, Chunhua and Jiang, Yuning and Li, Lei}, booktitle = {Proc. Eur. Conf. Computer Vision (ECCV)}, year = {2020} } ``` ```BibTeX @inproceedings{wang2020solov2, title = {{SOLOv2}: Dynamic and Fast Instance Segmentation}, author = {Wang, Xinlong and Zhang, Rufeng and Kong, Tao and Li, Lei and Shen, Chunhua}, booktitle = {Proc. Advances in Neural Information Processing Systems (NeurIPS)}, year = {2020} } ```