Mirror paper baseline checkpoints from the review repo

README.md

@@ -31,6 +31,7 @@ README.md                                ← this file
 WildBox_train.json                       ← Omni3D-format train split
 WildBox_val.json                         ← Omni3D-format val   split
 metadata/                                ← per-segment / per-video / per-track statistics (CSV)
+checkpoints/                             ← paper baseline checkpoints (see checkpoints/CHECKPOINTS_README.md)
 <group>/                                 ← 11 group folders (see table below)
 └── <video>.zip                          ← 64 per-video archives total
 ```

checkpoints/CHECKPOINTS_README.md

@@ -0,0 +1,77 @@
+# Checkpoints
+
+This folder contains the model checkpoints corresponding to the headline
+WildBox results reported in the paper, one per architecture.
+
+```
+checkpoints/
+├── CHECKPOINTS_README.md
+├── detany3d_ep2_seed0/
+│   └── checkpoint_0.pth                 ← 4.1 GB — DetAny3D headline row
+└── ovmono3d_lift_init5sp_seed0/
+    └── model_final.pth                  ← 574 MB — OVMono3D-LIFT headline row
+```
+
+---
+
+## OVMono3D-LIFT — `ovmono3d_lift_init5sp_seed0/model_final.pth`
+
+* **Architecture:** OVMono3D-LIFT (Cube R-CNN backbone + GLIP open-vocabulary
+  2D detector, 3D head fine-tuned on WildBox).
+* **Training schedule:** 10 000 iters of 5-species pretraining (wildlife5)
+  → 10 000 iters of 6-species WildBox continuation = 20 000 total iters.
+* **Reproduces:** the row labelled *"init5sp curriculum, 10 000 iters"* in the
+  paper's fine-tuning table — macro 3D AP ≈ 13.17, BEV AP @ 0.50 ≈ 8.68 on
+  `WildBox_val_paper.json`.
+* **Format:** standard detectron2 / Cube R-CNN state dict.
+
+### How to load
+
+From the OVMono3D-LIFT eval repo:
+
+```bash
+python tools/train_net.py \
+    --eval-only \
+    --config-file configs/wildbox/OVMono3D_wildbox_wildlife6.yaml \
+    MODEL.WEIGHTS path/to/checkpoints/ovmono3d_lift_init5sp_seed0/model_final.pth
+```
+
+The other two seeds used to compute the ± std bar in that row are not shipped;
+they can be reproduced from the OVMono3D-LIFT eval repo with seeds 1 and 2.
+
+---
+
+## DetAny3D — `detany3d_ep2_seed0/checkpoint_0.pth`
+
+* **Architecture:** DetAny3D (SAM-ViT-H backbone + UniDepth depth prior +
+  multi-prompt 3D head, with Grounding-DINO oracle 2D for prompts).
+* **Training schedule:** 2-epoch fine-tune on WildBox training split, seed 0,
+  initialised from the upstream DetAny3D pretrained checkpoint. Note that
+  fine-tuning to 3 epochs **overfits** — see the ablation table in the paper.
+* **Reproduces:** the row labelled *"DetAny3D fine-tuned (2 epochs, oracle 2D)"*
+  in the paper's headline table.
+* **Format:** native DetAny3D `checkpoint_<epoch>.pth` (the file is at epoch 1,
+  i.e. *after* 2 training epochs — DetAny3D 0-indexes its checkpoint counter).
+
+### How to load
+
+From the DetAny3D eval repo (`tools/wildbox_final.sh` wraps the full eval):
+
+```bash
+torchrun --nproc_per_node=4 ./train.py \
+    --config_path detect_anything/configs/wildbox/wildbox_eval_oracle2d.yaml \
+    --resume path/to/checkpoints/detany3d_ep2_seed0/checkpoint_0.pth \
+    --inference-only
+```
+
+Other seeds are not shipped; the headline number in the paper is single-seed
+(see the paper's ablation table for the seed-1 / seed-2 spread reasoning).
+
+---
+
+**Release note (v1.1).** These are the paper's headline checkpoints, trained and
+evaluated against the paper-version annotation export. This repository ships the
+v1.1 annotation export (human-corrected tracks; see the dataset README), so
+re-evaluating these checkpoints against the v1.1 ground truth can yield numbers
+that differ from the paper's, which are reproduced against the paper-version
+ground truth.

checkpoints/detany3d_ep2_seed0/checkpoint_0.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d1b80cdfe88b5245c05c976667a6714809517742e7cefe27eeb40d7dfeed55a
+size 4395140579

checkpoints/ovmono3d_lift_init5sp_seed0/model_final.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d80cfb9a394d848ec0bbe74c5e7f8c294c443a521ab94a590c6d84594204efd8
+size 601426000