Upload pointmlp-base.scanobjectnn.xu-ma

README.md

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+---
+license: apache-2.0
+library_name: torch-pointcloud
+tags:
+- point-cloud
+- 3d
+- pytorch
+- torch-pointcloud
+- pointmlp
+- classification
+datasets:
+- scanobjectnn
+model-index:
+- name: pointmlp-base.scanobjectnn.xu-ma
+  results:
+  - task:
+      type: point-cloud-classification
+    dataset:
+      name: ScanObjectNN
+      type: scanobjectnn
+    metrics:
+    - name: OA
+      type: accuracy
+      value: 77.48
+---
+
+# Model card for pointmlp-base.scanobjectnn.xu-ma
+
+A PointMLP point cloud classification model (residual MLP with geometric affine grouping). Trained on ScanObjectNN.
+
+## Model Details
+
+- **Model Type:** Point cloud classification
+- **Model Stats:**
+  - Params (M): 13.2
+  - Input channels: 3
+  - Classes: 15
+  - Features: 1024
+- **Dataset:** ScanObjectNN
+- **Metrics:** OA 77.48 (reference 86.1)
+- **Paper:** [Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework](https://arxiv.org/abs/2202.07123)
+- **Converted from:** [ma-xu/pointMLP-pytorch](https://github.com/ma-xu/pointMLP-pytorch) (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(
+    "pointmlp-base.scanobjectnn.xu-ma",
+    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, 1024)
+```
+
+## Citation
+
+```bibtex
+@inproceedings{ma2022pointmlp,
+  title   = {Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework},
+  author  = {Xu Ma and Can Qin and Haoxuan You and Haoxi Ran and Yun Fu},
+  booktitle = {ICLR},
+  year    = {2022}
+}
+```
+
+```bibtex
+@inproceedings{uy2019scanobjectnn,
+  title   = {Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data},
+  author  = {Mikaela Angelina Uy and Quang-Hieu Pham and Binh-Son Hua and Duc Thanh Nguyen and Sai-Kit Yeung},
+  booktitle = {ICCV},
+  year    = {2019}
+}
+```

model.safetensors

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