LibreDETRr101

Original DETR with a ResNet-101 backbone (60.5M parameters, 43.5 box AP on COCO val2017 in the upstream model zoo), repackaged for LibreYOLO.

from libreyolo import LibreYOLO

model = LibreYOLO("LibreDETRr101.pt")
results = model.predict("image.jpg")

LibreYOLO ships this family for inference and validation, plus ONNX and TorchScript export. Training is not implemented. The deployment contract uses a fixed 800x800 canvas; upstream COCO evaluation instead preserves aspect ratio with a short side of 800 and a long side capped at 1333.

Source

Derived from the official facebookresearch/detr checkpoint detr-r101-2c7b67e5.pth at commit 29901c51d7fe8712168b8d0d64351170bc0f83e0. Copyright (c) Facebook, Inc. and its affiliates. Licensed under the Apache License 2.0.

Source checkpoint SHA-256: 2c7b67e52d2e687cefa3c4a5e701a148664159c96725c62984600d2d9d5c4104.

Modifications

Checkpoint metadata wrap only. Learned parameter names and tensors are unchanged. See weights/convert_detr_weights.py in the LibreYOLO source repository.

Strict state-dict loading succeeds with no missing or unexpected keys. Against the pinned upstream implementation, identical input tensors produce exact FP32 outputs (max_abs_diff == 0.0) for both pred_logits and pred_boxes. LibreYOLO maps the sparse COCO category ids to its contiguous 80-class public interface and does not apply NMS.

License

Apache License 2.0. See the LICENSE and NOTICE files in this repository.