LibreRTMDetm-seg
RTMDet-Ins-m COCO instance segmenter, repackaged for the LibreYOLO framework.
Source
Derived from https://github.com/open-mmlab/mmdetection at commit cfd5d3a985b0249de009b67d04f37263e11cdf3d and upstream checkpoint:
https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet-ins_m_8xb32-300e_coco/rtmdet-ins_m_8xb32-300e_coco_20221123_001039-6eba602e.pth
(SHA-256 6eba602e5fb98ee993cffb1724bd6d51d2e86a69f261147f405e5582ad0098c1).
Copyright (c) 2018-2023 OpenMMLab. Licensed under the Apache License, Version 2.0.
Modifications
EMA weights were selected from the upstream checkpoint. data_preprocessor.* and batch-tracking buffers were omitted, bbox_head. keys were renamed to head., and the loaded state dict was saved with LibreYOLO checkpoint metadata schema v1.0 (task=segment). Learned model parameters are otherwise preserved.
Validation
Evaluated with LibreYOLO on full COCO val2017 (5000 images) at imgsz=640, conf=0.001, next to the official mmdetection references:
| Metric | LibreYOLO | Official |
|---|---|---|
| COCO val2017 mask mAP50-95 | 0.4208 | 42.1 |
| COCO val2017 box mAP50-95 | 0.4881 | 48.8 |
| SHA256 | bd0c615739c58a3fcbfb783c927ecfae57f0aa55c0d5ecded1e4b0fa996acb7f |
Usage
from libreyolo import LibreYOLO
model = LibreYOLO("LibreRTMDetm-seg.pt")
res = model.predict("image.jpg")
res.masks # instance masks
res.boxes # boxes, scores, classes
License
Apache License 2.0. See the LICENSE and NOTICE files in this repository.