Welcome to the fascinating world of 3D Object Detection! In this guide, we will explore how to effectively use the Det3D toolbox in PyTorch. You’ll learn about its features, installation process, model zoo, and how to troubleshoot any issues you might encounter along the way. Let’s dive in!
1. Introduction
Det3D is a cutting-edge toolbox for 3D Object Detection that provides out-of-the-box implementations of several advanced algorithms like PointPillars, SECOND, and PIXOR, along with state-of-the-art methods on significant datasets such as KITTI and nuScenes. Here are some key features:
- Multi Datasets Support: KITTI, nuScenes, Lyft
- Point-based and Voxel-based model zoo
- State-of-the-art performance
- Distributed Data Parallel (DDP) SyncBN
2. Installation
For detailed instructions on installing Det3D, refer to the INSTALLATION.md file.
3. Quick Start
Get started with Det3D effortlessly by following the guidelines available in the GETTING_STARTED.md file.
4. Model Zoo
The Det3D toolbox boasts a comprehensive model zoo for various datasets. Below are some high-performance models:
4.1 nuScenes
- CBGS:
- mAP: 49.9
- NDS: 61.3
- Configuration Link
- Download Link
- PointPillar:
- mAP: 41.8
- NDS: 56.0
- Configuration Link
- Download Link
4.2 KITTI
- SECOND:
- AP@0.70: Bbox AP: 90.54, 89.35, 88.43
- Configuration Link
- PointPillars:
- AP@0.70: Bbox AP: 90.63, 88.86, 87.35
- Configuration Link
5. Functionality
Det3D offers a rich array of functionalities:
- Models: VoxelNet, SECOND, PointPillars
- Multi-task Learning
- Distributed Training and Validation
- SyncBN, Flexible anchor dimensions, TensorboardX
6. TODO List
The team behind Det3D intends to enhance the toolbox further with:
- Support for the Waymo Dataset
- Integration of additional 3D detection models such as VoteNet, and PIXOR
7. Troubleshooting
If you run into issues while using Det3D, here are a few troubleshooting ideas:
- Double-check dependencies and ensure they are correctly installed.
- Make sure your dataset paths are accurate and accessible.
- If your models are not performing as expected, review the configuration file settings.
- Check online forums for solutions or new contributions from the community.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
8. Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
