| --- |
| license: cc-by-nc-4.0 |
| tags: |
| - pytorch |
| - point-cloud |
| - point-cloud-completion |
| - shapenet |
| - adapointr |
| - 3d |
| --- |
| |
| # Matched-control audit of naïve difficulty conditioning: AdaPoinTr-family checkpoints |
|
|
| Checkpoints for *Naïve Difficulty Conditioning Fails on ShapeNet-55: A Matched-Control Audit* (Wenbo Gong, Transactions on Machine Learning Research, 2026). |
|
|
| - Paper and reviews: https://openreview.net/forum?id=cpe5PHAgoQ |
| - Code, configurations, logs and result artefacts: https://github.com/grenbel/diffcond-matched-control-audit |
|
|
| These are the 26 retained AdaPoinTr-family checkpoints listed with their SHA-256 in the supplementary material (Table 14). The files are byte-identical to that manifest; `checkpoints.sha256` gives the digest of every file under its path here. |
|
|
| ## Files |
|
|
| | File in the Hugging Face repository | Role in the paper | Seed | Epoch | Configuration (`cfgs/ShapeNet55_models/`) | Size (MB) | |
| |---|---|---|---|---|---| |
| | `baseline_300ep/ckpt-best.pth` | Baseline (300-epoch from-scratch AdaPoinTr; warm-start source) | – | best | `AdaPoinTr.yaml` | 389.5 | |
| | `vanilla_ft/seed42/ckpt-epoch-030.pth` | Vanilla-FT (matched control) | 42 | 30 | `AdaPoinTr_baseline_smoke30_diffcrop.yaml` | 389.5 | |
| | `vanilla_ft/seed43/ckpt-epoch-030.pth` | Vanilla-FT (matched control) | 43 | 30 | `AdaPoinTr_baseline_smoke30_diffcrop.yaml` | 389.5 | |
| | `vanilla_ft/seed44/ckpt-epoch-030.pth` | Vanilla-FT (matched control) | 44 | 30 | `AdaPoinTr_baseline_smoke30_diffcrop.yaml` | 389.5 | |
| | `diffcond_full/seed42/ckpt-epoch-030.pth` | DiffCond (full) | 42 | 30 | `AdaPoinTr_DiffCond_smoke30.yaml` | 396.9 | |
| | `diffcond_full/seed43/ckpt-epoch-030.pth` | DiffCond (full) | 43 | 30 | `AdaPoinTr_DiffCond_smoke30.yaml` | 396.9 | |
| | `diffcond_full/seed44/ckpt-epoch-030.pth` | DiffCond (full) | 44 | 30 | `AdaPoinTr_DiffCond_smoke30.yaml` | 396.9 | |
| | `ablation_v1_lambda0/seed42/ckpt-epoch-030.pth` | Ablation v1-λ0 (CE off, ranking on) | 42 | 30 | `AdaPoinTr_DiffCond_v1_lambda0.yaml` | 395.9 | |
| | `ablation_v1_lambda0/seed43/ckpt-epoch-030.pth` | Ablation v1-λ0 (CE off, ranking on) | 43 | 30 | `AdaPoinTr_DiffCond_v1_lambda0.yaml` | 395.9 | |
| | `ablation_v1_lambda0/seed44/ckpt-epoch-030.pth` | Ablation v1-λ0 (CE off, ranking on) | 44 | 30 | `AdaPoinTr_DiffCond_v1_lambda0.yaml` | 395.9 | |
| | `ablation_v2b/seed42/ckpt-epoch-030.pth` | Ablation v2b (CE off, ranking off) | 42 | 30 | `AdaPoinTr_DiffCond_v2b.yaml` | 395.9 | |
| | `ablation_v2b/seed43/ckpt-epoch-030.pth` | Ablation v2b (CE off, ranking off) | 43 | 30 | `AdaPoinTr_DiffCond_v2b.yaml` | 395.9 | |
| | `ablation_v2b/seed44/ckpt-epoch-030.pth` | Ablation v2b (CE off, ranking off) | 44 | 30 | `AdaPoinTr_DiffCond_v2b.yaml` | 395.9 | |
| | `hard_specialist/seed42/ckpt-epoch-030.pth` | Hard-Specialist | 42 | 30 | `AdaPoinTr_hard_specialist.yaml` | 389.5 | |
| | `hard_specialist/seed43/ckpt-epoch-030.pth` | Hard-Specialist | 43 | 30 | `AdaPoinTr_hard_specialist.yaml` | 389.5 | |
| | `hard_specialist/seed44/ckpt-epoch-030.pth` | Hard-Specialist | 44 | 30 | `AdaPoinTr_hard_specialist.yaml` | 389.5 | |
| | `easywide_specialist/seed42/ckpt-epoch-030.pth` | EasyWide-Specialist | 42 | 30 | `AdaPoinTr_easywide_specialist.yaml` | 389.5 | |
| | `easywide_specialist/seed43/ckpt-epoch-030.pth` | EasyWide-Specialist | 43 | 30 | `AdaPoinTr_easywide_specialist.yaml` | 389.5 | |
| | `easywide_specialist/seed44/ckpt-epoch-030.pth` | EasyWide-Specialist | 44 | 30 | `AdaPoinTr_easywide_specialist.yaml` | 389.5 | |
| | `from_scratch_30ep_rerun/seed42/ckpt-best.pth` | From-scratch 30-epoch baseline rerun (Pitfall 2 audit) | 42 | best | `AdaPoinTr_baseline_smoke30.yaml` | 389.5 | |
| | `from_scratch_30ep_rerun/seed42/ckpt-epoch-029.pth` | From-scratch 30-epoch baseline rerun (Pitfall 2 audit) | 42 | 29 | `AdaPoinTr_baseline_smoke30.yaml` | 389.5 | |
| | `from_scratch_30ep_rerun/seed42/ckpt-epoch-030.pth` | From-scratch 30-epoch baseline rerun (Pitfall 2 audit) | 42 | 30 | `AdaPoinTr_baseline_smoke30.yaml` | 389.5 | |
| | `from_scratch_30ep_rerun/seed42/ckpt-last.pth` | From-scratch 30-epoch baseline rerun (Pitfall 2 audit) | 42 | last | `AdaPoinTr_baseline_smoke30.yaml` | 389.5 | |
| | `non_paper/vanilla_stock_crop/ckpt-epoch-030.pth` | Not used in the paper: early vanilla fine-tune, stock crop path | – | 30 | `AdaPoinTr_baseline_smoke30.yaml` | 389.5 | |
| | `non_paper/easy_specialist_pilot/ckpt-epoch-030.pth` | Not used in the paper: Easy-Specialist pilot | – | 30 | `AdaPoinTr_easy_specialist.yaml` | 389.5 | |
| | `non_paper/median_specialist_pilot/ckpt-epoch-030.pth` | Not used in the paper: Median-Specialist pilot | – | 30 | `AdaPoinTr_median_specialist.yaml` | 389.5 | |
|
|
| The three checkpoints in `non_paper/` are not used for any number in the paper. The UpTrans and PCN checkpoints of the cross-backbone experiments were not retained and are not here. |
|
|
| ## Use |
|
|
| - Each file is a PyTorch checkpoint dictionary with the keys `base_model` (weights), `optimizer`, `epoch`, `metrics` and `best_metrics`; it loads with `torch.load(path, map_location="cpu", weights_only=True)`. |
| - DiffCond and the two ablation conditions use `models/AdaPoinTr_DiffCond.py` from the GitHub repository; all other checkpoints use upstream PoinTr's `models/AdaPoinTr.py`. The configuration column names the file in the GitHub repository's `cfgs/ShapeNet55_models/` that each checkpoint was trained with, and its `REPRO.md` explains how to evaluate or fine-tune with upstream PoinTr at commit `3f98676aeaabd9c1150dd04cd0a51f504949ab9c`. |
| - Every fine-tuned checkpoint here was warm-started from `baseline_300ep/ckpt-best.pth`; `baseline_300ep/` and `from_scratch_30ep_rerun/` were trained from scratch. |
| - The `metrics` and `best_metrics` stored in the files are training-time validation values under PoinTr's random-crop validation proxy, not the paper's S/M/H results. |
|
|
| ## Verify a download |
|
|
| ```bash |
| hf download spacetime1008/diffcond-matched-control-audit --local-dir diffcond-ckpts |
| cd diffcond-ckpts && sha256sum -c checkpoints.sha256 |
| ``` |
|
|
| ## Licence |
|
|
| CC BY-NC 4.0. The models were trained on ShapeNet-55, which is derived from ShapeNet; using them must also comply with ShapeNet's terms of use (https://huggingface.co/spaces/ShapeNet/README/blob/main/TERMS.md), which allow non-commercial research and educational use only. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{gong2026naive, |
| title = {Na{\"i}ve Difficulty Conditioning Fails on {ShapeNet-55}: A Matched-Control Audit}, |
| author = {Gong, Wenbo}, |
| journal = {Transactions on Machine Learning Research}, |
| year = {2026}, |
| url = {https://openreview.net/forum?id=cpe5PHAgoQ} |
| } |
| ``` |
|
|
