Upload dgcnn.shapenetpart.an-tao

README.md

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+---
+license: mit
+library_name: torch-pointcloud
+tags:
+- point-cloud
+- 3d
+- pytorch
+- torch-pointcloud
+- dgcnn
+- segmentation
+datasets:
+- shapenetpart
+model-index:
+- name: dgcnn.shapenetpart.an-tao
+  results:
+  - task:
+      type: point-cloud-segmentation
+    dataset:
+      name: ShapeNetPart
+      type: shapenetpart
+    metrics:
+    - name: mIoU
+      type: mean_iou
+      value: 85.23
+---
+
+# Model card for dgcnn.shapenetpart.an-tao
+
+A DGCNN point cloud segmentation model (dynamic graph convolution over EdgeConv features). Trained on ShapeNetPart.
+
+## Model Details
+
+- **Model Type:** Point cloud semantic segmentation
+- **Model Stats:**
+  - Params (M): 1.5
+  - Classes: 50
+  - Features: 1280
+- **Dataset:** ShapeNetPart
+- **Metrics:** mIoU 85.23 (reference 85.2)
+- **Paper:** [Dynamic Graph CNN for Learning on Point Clouds](https://arxiv.org/abs/1801.07829)
+- **Converted from:** [antao97/dgcnn.pytorch](https://github.com/antao97/dgcnn.pytorch) (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(
+    "dgcnn.shapenetpart.an-tao",
+    task="segmentation",
+    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),
+    "category": torch.tensor(0),
+    "segment": torch.zeros(num_points, dtype=torch.long),
+}
+data = info["transform"](sample)
+data = collate([data])
+
+with torch.no_grad():
+    logits = model(data.get("x"), data["pos"], data["batch"], data["category"])
+```
+
+## Feature extraction
+
+```python
+with torch.no_grad():
+    features = model.forward_features(data.get("x"), data["pos"], data["batch"], data["category"])
+
+model.reset_classifier(num_classes=0)
+with torch.no_grad():
+    features = model(data.get("x"), data["pos"], data["batch"], data["category"])  # (N, 1280)
+```
+
+## Citation
+
+```bibtex
+@article{wang2019dgcnn,
+  title   = {Dynamic Graph CNN for Learning on Point Clouds},
+  author  = {Yue Wang and Yongbin Sun and Ziwei Liu and Sanjay E. Sarma and Michael M. Bronstein and Justin M. Solomon},
+  journal = {ACM Transactions on Graphics},
+  volume  = {38},
+  number  = {5},
+  year    = {2019}
+}
+```
+
+```bibtex
+@article{yi2016shapenetpart,
+  title   = {A Scalable Active Framework for Region Annotation in {3D} Shape Collections},
+  author  = {Yi, Li and Kim, Vladimir G. and Ceylan, Duygu and Shen, I-Chao and Yan, Mengyan and Su, Hao and Lu, Cewu and Huang, Qixing and Sheffer, Alla and Guibas, Leonidas},
+  journal = {ACM Transactions on Graphics (TOG)},
+  volume  = {35},
+  number  = {6},
+  year    = {2016}
+}
+```

model.safetensors

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