license: other
library_name: pytorch
tags:
- occupancy-prediction
- semantic-occupancy
- embodied-ai
- occscannet
- adaocc
- radio
- depth-anything-v2
AdaOcc checkpoints
AdaOcc: Adaptive 3D Occupancy Prediction for Embodied Tasks
Project page / code / setup instructions: https://github.com/wangjl-nb/AdaOcc
This Hugging Face repository only hosts the public AdaOcc checkpoint assets. It does not include OccScanNet data, generated labels/depth maps, RADIO weights, or the Depth-Anything-V2 fine-tuned checkpoint.
Uploaded files
| file | description | target path in the AdaOcc repo |
|---|---|---|
pretrain/fusion_pretrain_model.pth |
Slim fusion pretrain initializer for training AdaOcc from scratch. | pretrain/fusion_pretrain_model.pth |
checkpoints/adaocc_online_depth_occscannet_mini_epoch200.pth |
Trained AdaOcc online-depth OccScanNet-mini epoch-200 checkpoint for direct evaluation. | checkpoints/adaocc_online_depth_occscannet_mini_epoch200.pth |
configs/radio_occscannet_mini_training_snapshot.py |
Config snapshot from the released training run. | reference only |
logs/online_depth_occscannet_mini_epoch200.log |
Training/evaluation log for the released checkpoint. | reference only |
SHA256SUMS |
Checksums for hosted assets. | reference only |
The released evaluation checkpoint reports mIoU=58.49 and IoU=65.49 on OccScanNet-mini; see the uploaded log for the full validation line.
Download
Run from the AdaOcc GitHub repository root:
hf download wjldragon/AdaOcc \
pretrain/fusion_pretrain_model.pth \
checkpoints/adaocc_online_depth_occscannet_mini_epoch200.pth \
--local-dir .
Use --local-dir . so the checkpoint paths are restored exactly under pretrain/ and checkpoints/. Do not use --local-dir pretrain or --local-dir checkpoints, which would create nested paths such as pretrain/pretrain/....
Fusion pretrain note
pretrain/fusion_pretrain_model.pth is a slim OPUS-derived initializer. It keeps the sparse middle-encoder weights used by AdaOcc's public config (pts_middle_encoder.*) and removes unused branches. The extraction script is in the GitHub project at scripts/extract_adaocc_fusion_pretrain.py.
For data preparation, environment setup, training, evaluation, and upstream asset instructions, please use the GitHub project: https://github.com/wangjl-nb/AdaOcc.