Datasets:
File size: 3,058 Bytes
949e238 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | # 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.
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