Upload kpfcnn-base.s3dis.hugues-thomas

README.md

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+---
+license: mit
+library_name: torch-pointcloud
+tags:
+- point-cloud
+- 3d
+- pytorch
+- torch-pointcloud
+- kpconv
+- segmentation
+datasets:
+- s3dis
+model-index:
+- name: kpfcnn-base.s3dis.hugues-thomas
+  results:
+  - task:
+      type: point-cloud-segmentation
+    dataset:
+      name: S3DIS (6-fold)
+      type: s3dis
+    metrics:
+    - name: mIoU
+      type: mean_iou
+      value: 66.44
+---
+
+# Model card for kpfcnn-base.s3dis.hugues-thomas
+
+A KPConv point cloud segmentation model (deformable kernel point convolution). Trained on S3DIS (6-fold).
+
+## Model Details
+
+- **Model Type:** Point cloud semantic segmentation
+- **Model Stats:**
+  - Params (M): 24.4
+  - Input channels: 5
+  - Classes: 13
+  - Features: 128
+- **Dataset:** S3DIS (6-fold)
+- **Metrics:** mIoU 66.44 (reference 66.4)
+- **Paper:** [KPConv: Flexible and Deformable Convolution for Point Clouds](https://arxiv.org/abs/1904.08889)
+- **Converted from:** [HuguesTHOMAS/KPConv-PyTorch](https://github.com/HuguesTHOMAS/KPConv-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(
+    "kpfcnn-base.s3dis.hugues-thomas",
+    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),
+    "color": torch.rand(num_points, 3) * 255,
+    "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"])
+```
+
+## Feature extraction
+
+```python
+with torch.no_grad():
+    features = model.forward_features(data.get("x"), data["pos"], data["batch"])
+
+model.reset_classifier(num_classes=0)
+with torch.no_grad():
+    features = model(data.get("x"), data["pos"], data["batch"])  # (N, 128)
+```
+
+## Citation
+
+```bibtex
+@inproceedings{thomas2019kpconv,
+  title   = {KPConv: Flexible and Deformable Convolution for Point Clouds},
+  author  = {Hugues Thomas and Charles R. Qi and Jean-Emmanuel Deschaud and Beatriz Marcotegui and François Goulette and Leonidas J. Guibas},
+  booktitle = {ICCV},
+  year    = {2019}
+}
+```
+
+```bibtex
+@inproceedings{armeni2016s3dis,
+  title     = {{3D} Semantic Parsing of Large-Scale Indoor Spaces},
+  author    = {Armeni, Iro and Sener, Ozan and Zamir, Amir R. and Jiang, Helen and Brilakis, Ioannis and Fischer, Martin and Savarese, Silvio},
+  booktitle = {CVPR},
+  year      = {2016}
+}
+```

model.safetensors

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