CodeCamp #43 Support PETR in 1.1 in projects

petr

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+[dev-1.x-project aca498fd] petr3d-
+ 3 files changed, 5 insertions(+), 5 deletions(-)

projects/PETR/README.md

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+# PETR
+
+This is an README for `PETR`.
+
+## Description
+
+Author: @SekiroRong.
+This is an implementation of *PETR*.
+
+## Usage
+
+<!-- For a typical model, this section should contain the commands for training and testing. You are also suggested to dump your environment specification to env.yml by `conda env export > env.yml`. -->
+
+### Training commands
+
+In MMDet3D's root directory, run the following command to train the model:
+
+```bash
+python tools/train.py projects/PETR/config/petr/petr_vovnet_gridmask_p4_800x320.py
+```
+
+### Testing commands
+
+In MMDet3D's root directory, run the following command to test the model:
+
+```bash
+python tools/test.py projects/PETR/config/petr/petr_vovnet_gridmask_p4_800x320.py ${CHECKPOINT_PATH}
+```
+
+## Results
+
+<!-- List the results as usually done in other model's README. [Example](https://github.com/open-mmlab/mmdetection3d/edit/dev-1.x/configs/fcos3d/README.md)
+ You should claim whether this is based on the pre-trained weights, which are converted from the official release; or it's a reproduced result obtained from retraining the model in this project. -->
+
+This Result is trained by petr_vovnet_gridmask_p4_800x320.py and use [weights](https://drive.google.com/file/d/1ABI5BoQCkCkP4B0pO5KBJ3Ni0tei0gZi/view?usp=sharing) as pretrain weight.
+
+|                                             Backbone                                             | Lr schd | Mem (GB) | Inf time (fps) | mAP  | NDS  |         Download         |
+| :----------------------------------------------------------------------------------------------: | :-----: | :------: | :------------: | :--: | :--: | :----------------------: |
+| [petr_vovnet_gridmask_p4_800x320](projects/PETR/configs/petr/petr_vovnet_gridmask_p4_800x320.py) |   1x    |   7.62   |      18.7      | 38.3 | 43.5 | [model](<>) \| [log](<>) |
+
+```
+mAP: 0.3830
+mATE: 0.7547
+mASE: 0.2683
+mAOE: 0.4948
+mAVE: 0.8331
+mAAE: 0.2056
+NDS: 0.4358
+Eval time: 118.7s
+
+Per-class results:
+Object Class	  AP	  ATE	  ASE	  AOE	  AVE	  AAE
+car	  0.567	  0.538	  0.151	  0.086	  0.873	  0.212
+truck	  0.341	  0.785	  0.213	  0.113	  0.821	  0.234
+bus	  0.426	  0.766	  0.201	  0.128	  1.813	  0.343
+trailer 0.216	  1.116	  0.227	  0.649	  0.640	  0.122
+construction_vehicle	  0.093	  1.118	  0.483	  1.292	  0.217	  0.330
+pedestrian	  0.453	  0.685	  0.293	  0.644	  0.535	  0.238
+motorcycle	  0.374	  0.700	  0.253	  0.624	  1.291	  0.154
+bicycle	      0.345	  0.622	  0.262	  0.775	  0.475	  0.011
+traffic_cone	  0.539	  0.557	  0.319	  nan	  nan	  nan
+barrier	      0.476	  0.661	  0.279	  0.142	  nan	  nan
+```