checkpoints/CHECKPOINTS_README.md

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:

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):

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.