Point2RBox-v3 (Jittor reproduction)
This is a
Jittor reproduction of
Point2RBox-v3
(point-supervised oriented object detection with SAM-guided pseudo-label
refinement,
arXiv: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 modelcheckpoints/rotated_fcos_1x_dota_using_pseudo_ckpt_12.pkl — stage-2 modelweights/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
- 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.
- Upstream's copy-paste stage-2 branch is dead code (condition can never be
true); reproduced verbatim.
num_workers=0 instead of 2 (Jittor multi-process dataloader deadlock);
pure infra, no effect on training math.- 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.
