| # 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. |
|
|
