Upload pointpillars-multihead.nuscenes.openpcdet

README.md

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+---
+license: apache-2.0
+library_name: torch-pointcloud
+tags:
+- point-cloud
+- 3d
+- pytorch
+- torch-pointcloud
+- pointpillars
+- object-detection
+datasets:
+- nuscenes
+---
+
+# Model card for pointpillars-multihead.nuscenes.openpcdet
+
+A PointPillars 3D object detection model (pillar encoder with a 2D backbone). Trained on nuScenes.
+
+## Model Details
+
+- **Model Type:** 3D object detection
+- **Model Stats:**
+  - Params (M): 6.1
+  - Input channels: 5
+  - Classes: 10
+  - Features: 384
+- **Dataset:** nuScenes
+- **Paper:** [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://arxiv.org/abs/1812.05784)
+- **Converted from:** [open-mmlab/OpenPCDet](https://github.com/open-mmlab/OpenPCDet) (Apache-2.0)
+- **Library:** [torch-pointcloud](https://github.com/arthurdjn/pytorch-pointcloud)
+
+## Install
+
+```bash
+pip install torch-pointcloud
+```
+
+## Usage
+
+```python
+import torch
+import torch_pointcloud as tp
+from torch_pointcloud.utils.data import collate
+
+model, info = tp.create_model(
+    "pointpillars-multihead.nuscenes.openpcdet",
+    task="detection",
+    pretrained=True,
+    return_info=True,
+)
+model = model.eval()
+
+# synthetic sample with the keys a dataset provides
+num_points = 8192
+sample = {
+    "pos": torch.randn(num_points, 3),
+    "intensity": torch.rand(num_points, 1),
+    "timestamp": torch.zeros(num_points, 1),
+}
+data = info["transform"](sample)
+data = collate([data], batch_from="pos_voxel")
+
+with torch.no_grad():
+    out = model(data["voxel"], data["pos_voxel"], data["voxel_num_points"], data["batch"])
+```
+
+## Feature extraction
+
+```python
+with torch.no_grad():
+    features = model.forward_features(
+        data["voxel"],
+        data["pos_voxel"],
+        data["voxel_num_points"],
+        data["batch"],
+    )  # 384 channels
+```
+
+## Citation
+
+```bibtex
+@inproceedings{lang2019pointpillars,
+  title   = {PointPillars: Fast Encoders for Object Detection from Point Clouds},
+  author  = {Alex H. Lang and Sourabh Vora and Holger Caesar and Lubing Zhou and Jiong Yang and Oscar Beijbom},
+  booktitle = {CVPR},
+  year    = {2019}
+}
+```
+
+```bibtex
+@inproceedings{caesar2020nuscenes,
+  title   = {nuScenes: A multimodal dataset for autonomous driving},
+  author  = {Holger Caesar and Varun Bankiti and Alex H. Lang and Sourabh Vora and Venice Erin Liong and Qiang Xu and Anush Krishnan and Yu Pan and Giancarlo Baldan and Oscar Beijbom},
+  booktitle = {CVPR},
+  year    = {2020}
+}
+```

model.safetensors

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