base on # Generalizable-BEV <div style="text-align:center;"> <img src="/Framework.png" style="width:95%;" /> </div> A plug-and-play BEV generalization framework that can leverage both unlabeled and labeled data. ## Get Started #### **step 1. Prepare the environment refer to [BEVDet](https://github.com/HuangJunJie2017/BEVDet).** #### **step 2. Prepare PDBEV repo by.** ```shell script git clone https://github.com/EnVision-Research/Generalizable-BEV.git cd Generalizable-BEV pip install -v -e . ``` #### **step 3. Prepare datasets:** The preparation of the dataset is actually to generate the corresponding index (pkl files), which can then be used with the dataset that we have created. ###### nuScenes dataset pkl file generation refers to [Details](https://github.com/HuangJunJie2017/BEVDet) ###### DeepAccident dataset pkl file generation refers to [Details](https://github.com/tianqi-wang1996/DeepAccident), and then use ./tools/Deepaccident_converter.py to convert to a uniform format. ###### Lyft use ./tools/Lyft_converter.py to convert to a uniform format. The pre-processed pkl of the three data sets can be downloaded directly [here]. #### **step 4. Train for domain generalization:** ``` bash tools/dist_train.sh $confige_file$ $Gpus_num$ ``` For example: ``` bash tools/dist_train.sh ./configs/PDBEV/pdbev-r50-cbgs-NUS2X-dg.py 8 # nuScenes as source domain, using 8 gpus bash tools/dist_train.sh ./configs/PDBEV/pdbev-r50-cbgs-LYFT2X-dg.py 8 # Lyft as source domain, using 8 gpus bash tools/dist_train.sh ./configs/PDBEV/pdbev-r50-cbgs-DA2X-dg.py 8 # DeepAccident as source domain, using 8 gpus ``` #### **step 5. Train for unsupervised domain adaptation:** ``` bash tools/dist_train.sh $confige_file$ c --checkpoint $the pretrained models on source domain$ ``` For example: ``` bash tools/dist_train.sh ./configs/PDBEV/pcbev-uda-NUS2LYFT.py 8 --checkpoint ./work_dirs/pdbev-r50-cbgs-NUS2X-dg/epoch_23.pth # nuScenes as source domain, LYFT as target domain, using 8 gpus, loading DG pretrain models at 23 epoch # You only need to modify the path of the configuration file of different data set D and the corresponding model M to test the performance of model M on the corresponding data set D. It is worth mentioning that none of our algorithms change the model infrastructure, so they are only used for BEVDepth evaluation. ``` #### **step 6. Test at target domain:** ``` bash ./tools/dist_test.sh &test dataset config_file& &model_path& $Gpus_num$ --eval bbox --out $output_path$ ``` For example: ``` bash ./tools/dist_test.sh ./configs/bevdet_our/bevdepth-r50-cbgs-pc-lyft.py ./work_dirs/pdbev-r50-cbgs-NUS2X-dg/epoch_24.pth 8 --eval bbox --out ./work_dirs/bevdepth-r50-cbgs-pc-nus/nus.pkl ``` ## Acknowledgement This project is not possible without multiple great open-sourced code bases. We list some notable examples: [BEVDet](https://github.com/HuangJunJie2017/BEVDet), [DeepAccident](https://github.com/tianqi-wang1996/DeepAccident), [Lyft](https://github.com/lyft/nuscenes-devkit). ## Email [email protected] ## Citation ``` @InProceedings{PD-BEV, author = {Hao LU, Yunpeng ZHANG, Qing LIAN, Dalong DU, Ying-Cong CHEN}, title = {Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing}, booktitle = {arXiv preprint arXiv:2310.11346}, year = {2023}, } ``` ", Assign "at most 3 tags" to the expected json: {"id":"6464","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"