Instructions to use LibreYOLO/LibreDFINEn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LibreYOLO
How to use LibreYOLO/LibreDFINEn 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
- Notebooks
- Google Colab
- Kaggle
LibreDFINEn
D-FINE-n (nano) detection weights, repackaged for LibreYOLO.
Reported COCO val2017 mAP50-95: 42.8.
Source
Derived from Peterande/D-FINE at the v1.0
release (dfine_n_coco.pth).
This checkpoint is trained directly on the COCO dataset (no Objects365 pretraining is published for this size).
Copyright (c) 2024 The D-FINE Authors. Licensed under the Apache License, Version 2.0.
The HGNetV2 backbone is copied from PaddlePaddle/PaddleClas. Copyright (c) 2024 PaddlePaddle Authors. Licensed under the Apache License, Version 2.0.
Modifications
Checkpoint metadata wrapping only β learned parameters are unchanged.
The wrapper adds model_family, size, nc, and names fields so the
unified LibreYOLO() factory routes correctly without filename heuristics.
See weights/convert_dfine_weights.py in the
LibreYOLO source repository.
Usage
pip install libreyolo
from libreyolo import LibreYOLO
model = LibreYOLO("LibreDFINEn.pt")
results = model("image.jpg", save=True)
License
Apache License 2.0. See the LICENSE and NOTICE
files in this repository.
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