Downloads · 30 days
0
LibreYOLO/LibreRetinaNetr50v2
LibreRetinaNetr50v2 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 bsd-3-clause.
RetinaNet (ResNet-50 FPN v2 with GroupNorm heads), repackaged for LibreYOLO. This is an inference-only model with 38,198,935 parameters.
Downloads · 30 days
0
Access
Public
Updated Aug 2, 2026
Repo size
153 MB
Likes
0
Public
Click a slice to open those files.
.pt153 MB · 100%
From the Hugging Face model README
RetinaNet (ResNet-50 FPN v2 with GroupNorm heads), repackaged for LibreYOLO. This is an inference-only model with 38,198,935 parameters.
from libreyolo import LibreYOLO
model = LibreYOLO("LibreRetinaNetr50v2.pt")
results = model.predict("image.jpg")
Derived from pytorch/vision at commit
336d36e8db990a905498c73933e35231876e28bc.
Copyright (c) Soumith Chintala 2016 and torchvision contributors. The source
implementation is BSD-3-Clause.
Official checkpoint: retinanet_resnet50_fpn_v2_coco-5905b1c5.pth
5905b1c544219215e544dbe319720397bc4e68de61a733a59350d7976645b7699ebdda7b2f496124233389bf42ea6efd906973b743392422a21757f94635c2f7train() raises.Checkpoint metadata was added for LibreYOLO's v1.0 schema. Learned tensors and
state-dict keys are unchanged. The native LibreYOLO graph strictly loads the
official state dict and has exact eager parity at every FPN feature, raw head,
and final detection. See weights/convert_retinanet_weights.py in the
LibreYOLO source repository.
The checkpoint publisher did not attach a separate per-object license file.
This mirror applies the releasing project's BSD-3-Clause license on an
implied, not publisher-confirmed, basis. Torchvision warns that pretrained
models may have their own licenses or terms derived from training data and
that users must determine whether they have permission for their use case.
COCO annotations are CC BY 4.0; source images retain their individual Flickr
terms. See LICENSE and NOTICE.