LibreRTMDetl Seg

--- license: apache-2.0 library_name: libreyolo tags: - object-detection - instance-segmentation - rtmdet - mmdetection --- # LibreRTMDetl-seg RTMDet-Ins-l COCO instance segmenter, repackaged for the [LibreYOLO](https://github.com/LibreYOLO/libreyolo) framework. ## Source Derived from [https://github.com/open-mmlab/mmdetection](https://github.com/open-mmlab/mmdetection) at commit `cfd5d3a985b0249de009b67d04f37263e11cdf3d` and upstream checkpoint: `https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet-ins_l_8xb32-300e_coco/rtmdet-ins_l_8xb32-300e_coco_20221124_103237-78d1d652.pth` (SHA-256 `78d1d6525c3065c3cf4f7033326f8a38ac6252fad28a7ff5f12e9db2d1a92b6a`). 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 | 0.4368 | 43.7 | | COCO val2017 box mAP50-95 | 0.5119 | 51.2 | | SHA256 | `2df3d44206b14c994cd86eca9a75c73a9dbb9acd00293f158dbf1d7da3967310` | | ## Usage ```python from libreyolo import LibreYOLO model = LibreYOLO("LibreRTMDetl-seg.pt") res = model.predict("image.jpg") res.masks # instance masks res.boxes # boxes, scores, classes ``` ## License Apache License 2.0. See the [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE) files in this repository.

Source context: 0 downloads · 1 likes · Pipeline object-detection · Library libreyolo · Repo LibreYOLO/LibreRTMDetl-seg