LibreFasterRCNNn

LibreFasterRCNNn

Modernized Faster R-CNN (MobileNetV3-Large 320-FPN low-resolution variant), repackaged for LibreYOLO. This is a torchvision COCO recipe, not the original 2015 VGG16 architecture.

from libreyolo import LibreYOLO

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

Source

Derived from pytorch/vision at commit 336d36e8db990a905498c73933e35231876e28bc. Copyright (c) Soumith Chintala 2016 and torchvision contributors. The source implementation is BSD-3-Clause.

Official checkpoint: fasterrcnn_mobilenet_v3_large_320_fpn-907ea3f9.pth SHA-256: 907ea3f91ff92242bc1baea8049276a3e76bca48ce7560bd268cc029f37977b5 Published COCO val2017 box mAP: 22.8.

Modifications

Checkpoint metadata was added for LibreYOLO's v1.0 schema. Learned tensors and state-dict keys are unchanged. The native LibreYOLO graph loads the official state dict strictly and has exact eager parity at the RPN head, RoI predictor, and final detections. See weights/convert_faster_rcnn_weights.py in the LibreYOLO source repository.

Benchmarks

Independent accuracy and speed benchmarks: visionanalysis.org/model/faster_rcnn-n

License

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.