Upload point-bert-base.modelnet40.xumin-yu

README.md

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+---
+license: mit
+library_name: torch-pointcloud
+tags:
+- point-cloud
+- 3d
+- pytorch
+- torch-pointcloud
+- point-bert
+- classification
+datasets:
+- modelnet40
+base_model: torch-pointcloud/point-bert-base.pretrain.xumin-yu
+model-index:
+- name: point-bert-base.modelnet40.xumin-yu
+  results:
+  - task:
+      type: point-cloud-classification
+    dataset:
+      name: ModelNet40
+      type: modelnet40
+    metrics:
+    - name: OA
+      type: accuracy
+      value: 92.63
+---
+
+# Model card for point-bert-base.modelnet40.xumin-yu
+
+A Point-BERT point cloud classification model (masked point modeling transformer). Trained on ModelNet40.
+
+## Model Details
+
+- **Model Type:** Point cloud classification
+- **Model Stats:**
+  - Params (M): 22.1
+  - Classes: 40
+  - Features: 768
+- **Dataset:** ModelNet40
+- **Metrics:** OA 92.63 (reference 92.67)
+- **Paper:** [Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling](https://arxiv.org/abs/2111.14819)
+- **Converted from:** [Julie-tang00/Point-BERT](https://github.com/Julie-tang00/Point-BERT) (MIT)
+- **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(
+    "point-bert-base.modelnet40.xumin-yu",
+    task="classification",
+    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),
+    "normal": torch.randn(num_points, 3),
+}
+data = info["transform"](sample)
+data = collate([data])
+
+with torch.no_grad():
+    logits = model(data.get("x"), data["pos"], data["batch"])
+```
+
+## Feature extraction
+
+```python
+with torch.no_grad():
+    embeddings = model.forward_features(data.get("x"), data["pos"], data["batch"])
+
+model.reset_classifier(num_classes=0)
+with torch.no_grad():
+    embeddings = model(data.get("x"), data["pos"], data["batch"])  # (B, 768)
+```
+
+## Citation
+
+```bibtex
+@inproceedings{yu2022pointbert,
+  title   = {Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling},
+  author  = {Xumin Yu and Lulu Tang and Yongming Rao and Tiejun Huang and Jie Zhou and Jiwen Lu},
+  booktitle = {CVPR},
+  year    = {2022}
+}
+```
+
+```bibtex
+@inproceedings{wu2015modelnet,
+  title   = {3D ShapeNets: A Deep Representation for Volumetric Shapes},
+  author  = {Zhirong Wu and Shuran Song and Aditya Khosla and Fisher Yu and Linguang Zhang and Xiaoou Tang and Jianxiong Xiao},
+  booktitle = {CVPR},
+  year    = {2015}
+}
+```

model.safetensors

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