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docs(how-to-guides): add training docs for centerpoint
Description
Prepare documentation about training lidar CenterPoint model
Blocked by:
- https://github.com/autowarefoundation/autoware.universe/pull/5570
- https://github.com/autowarefoundation/mmdetection3d/pull/1
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Hello @yukke42 -san, @miursh -san, @yukkysaito -san, @mitsudome-r -san
I prepared an overview section of lidar CenterPoint training under Autoware documentation and training steps under the "lidar_centerpoint" package.
Before we release the fork of the mmdetection3D and sample_dataset(Tier4 Format) under Autoware Foundation, please review the dataset and fork of the mmdetection3D repository. I am waiting for your feedback.
Fork of the mmdetection3D (dev-1.x-autoware branch): Fork of the mmdetection 3D contains,
- PyTorch to ONNX converter
- Point Encoder optional use_voxel_center_z parameter
- Tier4 dataset class
- Custom metric calculator including NuScenes evaluation metrics (Original repository just support NuScenes dataset)
link : https://github.com/kaancolak/mmdetection3d/tree/dev-1.x-autoware
Sample Dataset(Tier4 Format)
- Contains 600 lidar frames that are 3D labeled in 2 hz(6905 cars, 3951 pedestrians, 75 cyclists, 162 buses, and 326 trucks).
link: https://drive.google.com/drive/folders/15VeeXje1La0FwUyt1hU8pKXXfcCvRyiJ
@kaancolak FYI, I have created the fork of MMdetection under Autowarefoundation GitHub: https://github.com/autowarefoundation/mmdetection3d. You should have access to create branch as well so feel free to port your work to the fork.
@kaancolak I sincerely apologize for the delay in reviewing. I have noted some comments and change requests regarding the dataset:
- A smaller file size for the T4 dataset is preferable, as the current size is leading to prolonged download times.
- The directory structure for each dataset should adhere to the guidelines outlined in this document. Specifically, there should be an 'annotation/data/input_bag' directory directly under 'T4_dataset_ID' (or 'scene_name').
- If the annotation frequency (Hz) is lower than that of the data, frames that were not annotated should not be included in sample.json. Additionally, for those frames, the is_key_frame in sample_data.json should be set to false. This might be due to our tools bug.
- It appears that the calibration, especially between CAM_BACK and base_link, is not well-aligned. Is this OK?
Hi @miursh -san, thank you for your feedback. Following that, I plan to filter out non-annotated frames from the dataset. This will reduce the total size from 32 GB to approximately 6 GB and eliminate the need to deal with keyframes.
I will also inspect the calibration of the back camera. If there seems any issue, I can remove them from the dataset.
Miura-san (@miursh),
I reloaded the dataset based on your feedback. Can you take a look and give feedback?
The total size decreased by around half of the previous version. (~13 GB)
The back camera is mounted on the rear glass of the vehicle and that part can be opened and closed as a car luggage. Somehow the calibration seems distorted, especially at a far distance. I removed it from the dataset.
Dataset link: https://drive.google.com/drive/folders/1qpts1HlOT3PdvrV_dRd1Q9syY2fhl8pO
@kaancolak I apologize for the delay in responding as I was taking a few days off. The updated dataset looks good to me! Thank you for the change!
Thank you so much @miursh -san. I will ask to Fatih upload the dataset to AWS.
This pull request has been automatically marked as stale because it has not had recent activity.
Documentation URL: https://autowarefoundation.github.io/autoware-documentation/pr-471/ Modified URLs:
- https://autowarefoundation.github.io/autoware-documentation/pr-471/how-to-guides/training-machine-learning-models/training-models/
This pull request has been automatically marked as stale because it has not had recent activity.
Hi @beginningfan, could you also review this document?