| --- |
| license: apache-2.0 |
| tags: |
| - object-detection |
| - oriented-object-detection |
| - jittor |
| - remote-sensing |
| - dota |
| --- |
| |
| # Point2RBox-v3 (Jittor reproduction) |
|
|
| This is a **Jittor** reproduction of |
| [Point2RBox-v3](https://github.com/VisionXLab/Point2RBox-v3) |
| (point-supervised oriented object detection with SAM-guided pseudo-label |
| refinement, [arXiv:2509.26281](https://arxiv.org/pdf/2509.26281)), built on |
| JDet. Code: <https://github.com/mingqian-233/Point2RBox-v3-jittor>. |
|
|
| ## Results (DOTA-v1.0, official test server, mAP50) |
|
|
| | Model | Paper (PyTorch) | This repo (Jittor) | |
| |---|---|---| |
| | Point2RBox-v3 end-to-end (12 ep) | 59.61 | **59.52** | |
| | Point2RBox-v3 two-stage (rotated-FCOS) | 66.09 | **65.50** | |
|
|
| The end-to-end number is the official DOTA-v1.0 Task1 test-server mAP50 |
| (`0.5952330691632053`) and passes the project's paper−2.0 acceptance threshold |
| (57.61). Per-class AP table: TBD. |
|
|
| Local diagnostic only: the norm-eval-correct end-to-end checkpoint scores |
| **66.6056 mAP50** on the unmerged 1024×1024 trainval-patch protocol. The |
| same-machine full upstream PyTorch run scores **65.60**, while the published |
| upstream log reports **66.70**. This is not the official DOTA test-server |
| protocol; the official server result is reported in the table above. |
|
|
| The completed two-stage checkpoint scores **75.7029 mAP50** on the same local |
| trainval-patch diagnostic and **65.50 mAP50** on the official DOTA-v1.0 Task1 |
| test server. The latter passes the project's paper−2.0 acceptance threshold |
| of 64.09. |
|
|
| The generated stage-2 pseudo boxes were also matched directly against all |
| 245,953 trainval GT boxes. They achieve mean rotated IoU **0.7330**, |
| recall@0.5 **91.03%**, and recall@0.75 **56.58%**. The corresponding |
| official-code PyTorch v2 baseline is 0.7295 / 91.25% / 55.39%. |
|
|
| ## Files |
|
|
| - `checkpoints/point2rbox_v3_1x_dota_ckpt_12.pkl` — end-to-end model |
| - `checkpoints/rotated_fcos_1x_dota_using_pseudo_ckpt_12.pkl` — stage-2 model |
| - `weights/mobile_sam.pkl` — MobileSAM converted weights (439 tensors, numpy |
| pickle; native Jittor port, mask IoU 1.0000 vs PyTorch on CPU fp32) |
| - `weights/ted.pkl` — TED edge detector converted weights (parity 5.7e-6) |
| - `logs/` — training logs |
|
|
| ## Training environment |
|
|
| - Jittor 1.3.8.5, numpy 1.26.4 (pinned — numpy≥2 silently corrupts jt.array |
| inputs), CUDA 11.2 toolchain (g++-10), single A100 80GB, official |
| hyper-parameters verbatim (end-to-end batch=2; stage-2 batch=4; AdamW |
| 5e-5, grad-clip 35, 12 epochs, LinearLR warmup 500 iters + MultiStepLR |
| [8,11]). |
| - Config parity is locked by a zero-tolerance L0 test suite (17 tests, |
| including the 74-line per-class SAM rule table byte-copied from upstream). |
|
|
| ## Known differences from the upstream paper run |
|
|
| 1. **SAM attention bias**: the upstream `mobile_sam` package calls `eval()` |
| before `load_state_dict`, leaving TinyViT's non-persistent attention-bias |
| cache at its random initialisation — i.e. the upstream training used an |
| unseeded random attention bias (unreproducible across runs). This repo |
| loads the weights correctly. Direction of the effect on mAP is unknown |
| (expected neutral-to-positive). See `docs/config_parity.md`. |
| 2. Upstream's copy-paste stage-2 branch is dead code (condition can never be |
| true); reproduced verbatim. |
| 3. `num_workers=0` instead of 2 (Jittor multi-process dataloader deadlock); |
| pure infra, no effect on training math. |
| 4. GPU-mode numeric noise vs PyTorch golden is at the cuDNN-TF32 level |
| (~1e-3); CPU-mode strict-fp32 parity is 1e-5 to bit-exact per component. |
|
|
