Add model card, leaderboard, training configuration, and checksums

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 cstnetwork
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -0,0 +1,101 @@
+---
+license: mit
+library_name: pytorch
+pipeline_tag: feature-extraction
+tags:
+  - point-cloud
+  - 3d
+  - geometric-deep-learning
+  - constraint-prediction
+  - baseline
+datasets:
+  - ZXCCHENGXI/cstnet2_stage1_mini
+---
+
+# CSTNet2 Stage 1 Backbone Baselines
+
+This repository contains eight independently trained XYZ-only Stage 1 direct
+baselines. Each model uses one point-cloud backbone and four per-point heads to
+predict primitive type, direction, dimension, and location. The baselines do
+not load CSTNet2 Stage 1 weights and do not use instance embedding, clustering,
+or geometric primitive fitting.
+
+Source code: [xcheng-tsinghua/cstnet2](https://github.com/xcheng-tsinghua/cstnet2)
+
+Training dataset: [ZXCCHENGXI/cstnet2_stage1_mini](https://huggingface.co/datasets/ZXCCHENGXI/cstnet2_stage1_mini)
+
+## Training configuration
+
+- Points per sample: 2,048
+- Epochs: 80
+- Hardware: 4 GPUs with DDP
+- Precision: BF16 AMP
+- Per-GPU batch size: 32 (global batch size 128)
+- Seed: 2026
+- Learning rate: 1e-4
+- Weight decay: 1e-4
+
+All eight models were randomly initialized and trained independently with the
+same dataset and optimization settings.
+
+## Training-set leaderboard
+
+| Backbone | Best primitive mIoU | Best total loss |
+|---|---:|---:|
+| PointMLP | 0.654248 | 867.811967 |
+| DGCNN | 0.570031 | 867.934994 |
+| PointNet++ | 0.547805 | 867.963349 |
+| PointNeXt | 0.545523 | 867.983210 |
+| PointNet | 0.531966 | 867.991856 |
+| Attn3DGCN | 0.448774 | 867.927836 |
+| PointMamba | 0.391190 | 868.047581 |
+| PointTransformer | 0.385701 | 868.044473 |
+
+These values are **training-set metrics**. No held-out validation split was
+used for checkpoint selection, so they must not be interpreted as
+generalization results.
+
+## Repository layout
+
+```text
+<backbone>/seed_2026/
+  best_pmt_miou.pth
+  best_loss.pth
+  last.pth
+  history.json
+  config.json
+```
+
+The available backbone names are `pointnet2`, `pointnet`, `attn3dgcn`,
+`dgcnn`, `pointtransformer`, `pointmamba`, `pointnext`, and `pointmlp`.
+
+## Loading
+
+```python
+import torch
+
+from networks.stage1_direct_baselines import build_stage1_direct_baseline
+
+checkpoint = torch.load(
+    "pointmlp/seed_2026/best_pmt_miou.pth",
+    map_location="cpu",
+    weights_only=False,
+)
+model = build_stage1_direct_baseline(checkpoint["model_config"])
+model.load_state_dict(checkpoint["model"], strict=True)
+model.eval()
+```
+
+## Related repository
+
+The original standalone PointNet++ upload remains available at
+[FanMingyu/cstnet2-stage1-pointnet2-baseline](https://huggingface.co/FanMingyu/cstnet2-stage1-pointnet2-baseline).
+
+## Limitations
+
+- Reported scores are training metrics only.
+- The models were trained on mechanical CAD-derived point clouds and may not
+  generalize to noisy real-world scans.
+- Checkpoint files use Python/PyTorch serialization. Load only files from
+  trusted sources.
+

checksums.sha256

@@ -0,0 +1,32 @@
+b588edd758d676105bd14eb9de7b708f248f08f182bd075e07d8302aaa710e26  pointnet2/seed_2026/best_pmt_miou.pth
+aca79f4ed80b63e97bab0281e900d8dbc8c7724a6dc2de0f5527f1e16185fa72  pointnet2/seed_2026/best_loss.pth
+d7baad9eca967b316950543dae995fcd6aaa4fd4115fdc8daec3cb5d4b3cfcaf  pointnet2/seed_2026/last.pth
+8ff30faaff063cbc687e2c6b7e9f50704e86d9b649155650cded4e934a066f32  pointnet2/seed_2026/history.json
+1217b8366b57c17a2f42e78274a51153ddfd64118929ccb9d12fae32d7cf9003  pointnet/seed_2026/best_pmt_miou.pth
+eeb9e73ebea6fe1ca1d03b8fe529016dc55f364cf33b3f0234ef2657f6060479  pointnet/seed_2026/best_loss.pth
+2119b8d8394e259b065d76ed6fadd93a63327b9cf360274d5545aeb9278a3d8a  pointnet/seed_2026/last.pth
+f37dfc6357f28f6bea10e9f1a7bd58c54fbc5e22022195f07ee3d3d8bb29962b  pointnet/seed_2026/history.json
+719caadcf8c2c2ac6a784c72029a3400f3cf2e32d903d557efb518788eb12dde  attn3dgcn/seed_2026/best_pmt_miou.pth
+c3d794d3810e5960285c913d3cf1798a1d405c7ca00d607bb7ed68f93b7f8f4b  attn3dgcn/seed_2026/best_loss.pth
+634ce3c334d7759b1f6fec2a4f9eea0fe4836c26fa062a478992487a652bddbd  attn3dgcn/seed_2026/last.pth
+f8740d592396d148073863b32208c5b45e75ca46886e355e84c96eefec30b99f  attn3dgcn/seed_2026/history.json
+b289100c1bc1f4e93e40a5e7b6d8e295a0c1c4b0d469e666af6c294eb12478af  dgcnn/seed_2026/best_pmt_miou.pth
+d597e08fffdb575521b7aad97b8cffe9bfb1204630d2d609a2ee4a90d2c0963c  dgcnn/seed_2026/best_loss.pth
+1f6717ef361e5585bb42a21764fabebad960b74beaeba9ed98e5e6a97044290b  dgcnn/seed_2026/last.pth
+5433dc2e0a2a603021a8c9e43a6ea095f5a7d7f4c0f98709cc80ed8061628ff6  dgcnn/seed_2026/history.json
+6dbabe817adccc4d92077fcbf415dfe290c80f74cd2177d5d1689e6287adf12f  pointtransformer/seed_2026/best_pmt_miou.pth
+7b68a4ede51c42453cde4d6a37273c8201091fd71cfb2234677419ec1dc91446  pointtransformer/seed_2026/best_loss.pth
+a03d9f417290e6212a491c82514d5b46f22f51ceddb59628ff25db5e7db6224b  pointtransformer/seed_2026/last.pth
+26372f6b74361d732170fda6665d75edb477a61d262aa0d4d92df824023591a5  pointtransformer/seed_2026/history.json
+d851c93f1f506698bcffc028c06a855038ea1092b4a5da2eaf1009abbe354636  pointmamba/seed_2026/best_pmt_miou.pth
+5e385ad5fb0726aafc366aac5ff6d3f5f0cee327af937e665192be334b982d4e  pointmamba/seed_2026/best_loss.pth
+0e887216c437cd7dbca27c4bb321cde1dcd6cc7172823bb42550698b17594b23  pointmamba/seed_2026/last.pth
+4feb118a70a04769b0c9adab85c99bb1b8ddc945ab0a3ec9731e9f77b8c7f1be  pointmamba/seed_2026/history.json
+ed455b3989c9da256d8727d56295c7403d00769736f2fff29ecf1b5caa5646f1  pointnext/seed_2026/best_pmt_miou.pth
+98a06e87d103744a4ec381f52416da764c5abd52d68c838a0a6e97423a112469  pointnext/seed_2026/best_loss.pth
+1622455f300cd355533d89f519813b7433f7cae4d3f127e7f6b001461dacae31  pointnext/seed_2026/last.pth
+4b89bdce4a7b04b86bc6e2ef932a5cb194deb967616726e59f1660697620d2ed  pointnext/seed_2026/history.json
+3466f3fc68fe14d9322863546318f5ce7fd606b701f8c1e3752e56d9e7dad280  pointmlp/seed_2026/best_pmt_miou.pth
+f1882609d0f5d9135663204c64e522086e574228cc9b9f8f27b24a33f1544ac9  pointmlp/seed_2026/best_loss.pth
+72f916992ceaf0339febede8f36eb01b0cc09bddc8d37b3ee2f0ef52326d29c1  pointmlp/seed_2026/last.pth
+91ffc38115fe211649969207df1e49785801ec200d18a6af51c2273750e40c61  pointmlp/seed_2026/history.json

leaderboard.json

@@ -0,0 +1,10 @@
+[
+  {"backbone": "pointmlp", "best_pmt_miou": 0.654248, "best_loss": 867.811967},
+  {"backbone": "dgcnn", "best_pmt_miou": 0.570031, "best_loss": 867.934994},
+  {"backbone": "pointnet2", "best_pmt_miou": 0.547805, "best_loss": 867.963349},
+  {"backbone": "pointnext", "best_pmt_miou": 0.545523, "best_loss": 867.983210},
+  {"backbone": "pointnet", "best_pmt_miou": 0.531966, "best_loss": 867.991856},
+  {"backbone": "attn3dgcn", "best_pmt_miou": 0.448774, "best_loss": 867.927836},
+  {"backbone": "pointmamba", "best_pmt_miou": 0.391190, "best_loss": 868.047581},
+  {"backbone": "pointtransformer", "best_pmt_miou": 0.385701, "best_loss": 868.044473}
+]

training_config.json

@@ -0,0 +1,25 @@
+{
+  "model_type": "cstnet2_stage1_direct_backbone_baselines",
+  "architecture": "Stage1DirectBaseline",
+  "dataset": "ZXCCHENGXI/cstnet2_stage1_mini",
+  "backbones": [
+    "pointnet2",
+    "pointnet",
+    "attn3dgcn",
+    "dgcnn",
+    "pointtransformer",
+    "pointmamba",
+    "pointnext",
+    "pointmlp"
+  ],
+  "point_count": 2048,
+  "epochs": 80,
+  "precision": "bfloat16",
+  "world_size": 4,
+  "per_gpu_batch_size": 32,
+  "global_batch_size": 128,
+  "seed": 2026,
+  "learning_rate": 0.0001,
+  "weight_decay": 0.0001,
+  "source_code": "https://github.com/xcheng-tsinghua/cstnet2"
+}