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LibreYOLO/LibreDeformableDETRr50refine
LibreDeformableDETRr50refine is a object detection model from LibreYOLO. Use it when you need objects located in an image. It is set up for libreyolo. The card lists the license as apache-2.0.
The official iterative-box-refinement ResNet-50 Deformable DETR checkpoint (46.2 AP on COCO val2017), converted for LibreYOLO.
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Updated Aug 2, 2026
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From the Hugging Face model README
The official iterative-box-refinement ResNet-50 Deformable DETR checkpoint (46.2 AP on COCO val2017), converted for LibreYOLO.
from libreyolo import LibreYOLO
model = LibreYOLO("LibreDeformableDETRr50refine.pt")
results = model.predict("image.jpg")
LibreYOLO ships this museum family for inference only. Its portable deformable
attention uses the upstream pure-PyTorch grid_sample reference path; the
custom CUDA extension is not packaged. Native and ONNX inference use a fixed
800 x 800 PIL-bilinear stretch with ImageNet normalization. That deployment
transform intentionally differs from upstream's short-side-800/max-1333
evaluation transform.
Architecture derived from
fundamentalvision/Deformable-DETR
at commit 11169a60c33333af00a4849f1808023eba96a931.
Copyright (c) 2020 SenseTime. All Rights Reserved. Modified from DETR,
Copyright 2020 - present, Facebook, Inc. Licensed under Apache License 2.0.
The checkpoint comes from
SenseTime/deformable-detr-with-box-refine
at revision 2e9e461623a8fdc296e19666c46c8a4389a3a6fe.
Source model.safetensors SHA-256:
4113700fe8aade398808424b7c5c1304cfbf886adc6450a6ca5d50a702be3373.
Checkpoint keys are remapped to the original module layout, decoder Q/K/V projections are concatenated, and serialization aliases for the refinement heads are restored. Non-parameter BatchNorm tracking counters are omitted; learned tensor values are unchanged. The released 91-column COCO category-id head is retained and mapped to contiguous COCO-80 classes during postprocess.
The converted checkpoint SHA-256 is
84e4044553a306e07817bff9f147a50af3a51d16e43056160243a5a0d46d48c9.
LibreYOLO's output tensors are bit-exact against the pinned upstream
pure-PyTorch path (max_abs_diff == 0.0); fixed-800 ONNX Runtime prediction
parity is also verified.
See weights/convert_deformable_detr_weights.py and
docs/provenance/deformable_detr.md in the
LibreYOLO source repository.
Apache License 2.0. See the LICENSE and NOTICE
files in this repository.