FCOS3D

FCOS3D predicts three-dimensional boxes from a single image.

Task: 3d detection
Ukuran: r101 at 1600 px
Instalasi: pip install "libreyolo[hf]"
Tier dukungan: Tier sejajar, sejak v1.6.0. Permukaan produk terpisah dengan factory dan kontraknya sendiri.
Proyek upstream: FCOS3D oleh OpenMMLab, Apache-2.0 code. Makalah, sumber
Lisensi: Kode MIT, bobot No separate artifact grant recorded. Penggunaan komersial

Install

bash

pip install "libreyolo[hf]"

Predict

Python

from libreyolo import LibreFCOS3D, SAMPLE_IMAGEfrom PIL import Image model = LibreFCOS3D(device="cpu")# Rough pinhole guess so the snippet runs. For real metric boxes, pass# your camera's measured 3x3 matrix for the original image size.w, h = Image.open(SAMPLE_IMAGE).sizef = float(max(w, h))intrinsics = [[f, 0, w / 2], [0, f, h / 2], [0, 0, 1]]result = model.predict(SAMPLE_IMAGE, intrinsics=intrinsics)print(result.boxes3d)

FCOS3D runs natively on CPU or CUDA and requires original-image camera intrinsics. MPS is not supported. The default confidence is 0.05, rotated NMS IoU is 0.8 and max_det is 200.

result.boxes3d holds centers, dimensions and wxyz quaternions in camera coordinates, plus confidence, class and intrinsics. Plotting projects cuboids into the image. Training, validation, tracking and export are not supported. See 3D detection.

Licensing

Periksa lisensi di repositori Hugging Face untuk bobot tertentu yang diunduh. Setiap checkpoint di organisasi LibreYOLO memiliki lisensi, dan lisensinya tidak selalu sama dalam satu family. Repositori tersebut adalah sumber resmi. Ringkasan di bawah menjelaskan ketentuan yang berlaku saat halaman ini terakhir diverifikasi.

Ini adalah deskripsi lisensi yang terlibat, bukan nasihat hukum. Jika jawabannya penting secara komersial, baca sendiri lisensinya dan mintalah nasihat dari penasihat hukum Anda.

Karya asli: FCOS3D, OpenMMLab
Lisensi upstream: Apache-2.0 code
Sumber upstream: github.com/open-mmlab/mmdetection3d
Kode LibreYOLO: MIT
Bobot: No separate artifact grant recorded, dipublikasikan ulang di huggingface.co/LibreYOLO
Interpretasi: The upstream repository declares Apache-2.0. A distinct per-artifact checkpoint license is not recorded in the library source.

Citation

@inproceedings{wang2021fcos3d,
	title={{FCOS3D: Fully} Convolutional One-Stage Monocular 3D Object Detection},
	author={Wang, Tai and Zhu, Xinge and Pang, Jiangmiao and Lin, Dahua},
	booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
	year={2021}
}

Disalin dari blok sitasi penulis di raw.githubusercontent.com/open-mmlab/mmdetection3d/main/configs/fcos3d/README.md.