Instructions to use LibreYOLO/LibreWildDet3D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LibreYOLO
How to use LibreYOLO/LibreWildDet3D with LibreYOLO:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- WildDet3D
How to use LibreYOLO/LibreWildDet3D with WildDet3D:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
LibreWildDet3D
Mirror of the WildDet3D Stage 3 paper checkpoint for use with LibreYOLO's
optional LibreWildDet3D adapter.
License and use restrictions
This checkpoint is not covered by LibreYOLO's MIT license. It is provided under the custom SAM License, which includes field-of-use and trade-control restrictions. Read and comply with the complete license before downloading or using the checkpoint.
Source
- Publisher: allenai/WildDet3D
- Upstream repository revision:
1b8aa52 - Upstream Hugging Face revision:
161edb26239f58d8973045517d9c8b179c515f73 - Original filename:
wilddet3d_alldata_all_prompt_v1.0.pt - SHA-256:
f8b6a9e548f733ba62625a0d2adc4b0f4fdb6007ee11d9927f9c1027010fee57
The checkpoint is mirrored byte-for-byte. It contains the model's Stage 3 weights and retains its original filename and format. No tensor values, metadata, or serialization structure were changed.
Classes and prompts
WildDet3D has no fixed class list. It accepts text vocabularies such as
["car", "person"], as well as box and point prompts. Returned class ids index
the supplied text vocabulary or prompt order.
LibreYOLO usage
import numpy as np
from libreyolo import LibreWildDet3D
model = LibreWildDet3D()
result = model.predict(
"image.jpg",
intrinsics=np.load("intrinsics.npy"),
text=["car", "person"],
)
print(result.boxes3d.xyz)
The separately licensed WildDet3D runtime must be installed. On macOS,
LibreYOLO uses an isolated CPU worker; see the LibreYOLO detect3d contract for
the tested setup. MPS is not supported by the upstream image path. Omitting
model_path downloads this revision-pinned mirror and verifies its SHA-256;
an explicit local upstream checkpoint remains supported.
Validation
Using ten identical images, prompts, calibrations, thresholds, and this checkpoint:
- LibreYOLO CUDA matched direct upstream CUDA exactly for all returned numeric
fields (
max_abs_diff == 0.0). - LibreYOLO macOS CPU selected the same detections and classes in all ten cases. Maximum CPU/CUDA drift was 0.173 pixels for 2D boxes and 0.0374 metres for 3D centers.
This verifies adapter parity. It is not a 3D accuracy benchmark; the comparison images did not provide calibrated camera intrinsics or 3D ground truth.
License
See LICENSE for the complete SAM License and NOTICE
for provenance and mirroring details.
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