---
license: other
license_name: nuscenes-non-commercial
license_link: https://www.nuscenes.org/terms-of-use
library_name: libreyolo
pipeline_tag: object-detection
tags:
  - fcos3d
  - 3d-detection
  - monocular
  - nuscenes
  - mmdetection3d
  - non-commercial
  - pytorch
---

# LibreFCOS3D

Byte-identical mirror of the official FCOS3D R101-DCN nuScenes checkpoint
(finetuned variant), for use with LibreYOLO's `LibreFCOS3D` inference adapter.

> ## ⚠️ NON-COMMERCIAL WEIGHTS, AND AN IMPLIED REDISTRIBUTION BASIS
>
> **These weights are not covered by LibreYOLO's MIT license.** Two separate
> restrictions apply, and both bind you, the downloader:
>
> 1. **Training data.** This checkpoint was trained solely on
>    [nuScenes](https://www.nuscenes.org/nuscenes), whose
>    [terms of use](https://www.nuscenes.org/terms-of-use) are
>    **non-commercial**. Read them before use.
> 2. **No declared checkpoint license.** OpenMMLab publishes this file from its
>    model zoo without any per-object license. The mmdetection3d repository
>    states only that "this project is released under the Apache 2.0 license",
>    which covers the *code*. LibreYOLO's maintainer approved this mirror by
>    treating that project-level Apache-2.0 statement as the redistribution
>    basis. **This is LibreYOLO's disclosed interpretation, not a
>    clarification from the OpenMMLab authors.**
>
> The FCOS3D *architecture* and LibreYOLO's *code* are unrestricted, as is any
> model you train from scratch. Only these pretrained weights are limited.

## Source

- Publisher: [open-mmlab/mmdetection3d](https://github.com/open-mmlab/mmdetection3d)
  model zoo
- Original URL:
  [`fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth`](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth)
- SHA-256: `8d806dc2ecae85bc8eaba1f16dccdf03459317ca6aed7984cfda33c2a2bc33a8`
- Size: 220,360,589 bytes
- Published July 2021.

This is the **finetuned** variant (depth weight 1.0), not the 1x base model.
Published nuScenes val results: **32.1 mAP, 39.5 NDS**.

The LibreYOLO implementation that consumes it was adapted from
mmdetection3d at
[`fe25f7a`](https://github.com/open-mmlab/mmdetection3d/tree/fe25f7a51d36e3702f961e198894580d83c4387b)
and mmdetection at
[`cfd5d3a`](https://github.com/open-mmlab/mmdetection/tree/cfd5d3a985b0249de009b67d04f37263e11cdf3d),
both Apache-2.0.

## Modifications

**None.** The checkpoint is mirrored byte-for-byte, retaining its original
filename, `state_dict`/`meta` structure and serialization. No tensor values or
metadata were changed. It is *not* converted to LibreYOLO's v1.0 checkpoint
schema — the adapter loads the unchanged official format directly.

## Classes

The ten nuScenes detection classes, in the official order. LibreYOLO validates
the checkpoint's `meta.CLASSES` against that order and refuses a mismatch.

## LibreYOLO usage

```python
import numpy as np
from libreyolo import LibreFCOS3D

model = LibreFCOS3D("fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth")
result = model.predict("image.jpg", intrinsics=np.load("intrinsics.npy"))
print(result.boxes3d.xyz)
```

FCOS3D is a sibling API, not part of the generic `LibreYOLO(...)` factory,
because prediction requires original-image pinhole camera intrinsics. Download
this file and pass its local path. Inference is CPU or CUDA; MPS is
unsupported (torchvision `deform_conv2d`). Training, validation, tracking and
export are not implemented.

## Validation

LibreYOLO's native implementation was verified against the pinned upstream
Python implementation using this checkpoint: every `state_dict` key and shape
loads strictly, and final CPU detections match across six calibrated nuScenes
camera views at confidence thresholds 0.05, 0.15 and 0.30. This is adapter
parity, not an independent 3D accuracy benchmark; use the official nuScenes
evaluator for accuracy.

## License

See [`LICENSE`](./LICENSE) for the upstream Apache-2.0 code license and the
checkpoint's separate terms, and [`NOTICE`](./NOTICE) for provenance and
mirroring details.