LIS-models: Verified Checkpoints for Instance Segmentation in the Dark (IJCV 2023)

Official checkpoints for the IJCV 2023 paper "Instance Segmentation in the Dark" (Chen, Fu, Wei, Zheng, Heide). Every checkpoint below was re-evaluated on a single NVIDIA RTX 3090 with the released code (mmdetection 2.15.1 fork + mmcv-full 1.4.8 + PyTorch 1.11.0+cu113) before publishing; the numbers match the paper within 0.1 AP.

论文《Instance Segmentation in the Dark》(IJCV 2023) 官方权重。发布前每个权重均用开源代码（mmdetection 2.15.1 fork + mmcv-full 1.4.8 + PyTorch 1.11.0+cu113）在单卡 RTX 3090 上复评，数字与论文一致（最大偏差 0.1 AP）。

Code: github.com/Linwei-Chen/LIS · Dataset: Linwei-Chen/LIS

Checkpoints / 权重清单

Notation: segm / bbox = AP^mask / AP^bbox on the LIS test protocol (dark side).

Our method (AWD + SCB + DSL) / 论文方法

File	Size	Paper	Re-eval	Config (in repo)
mask_rcnn_r50_fpn_caffe_AWD_SCB_DSL_SynCOCO2LIS=31.8.pth	369M	31.8 / 37.6	31.8 / 37.6 ✅	Configs/mask_rcnn_r50_fpn_caffe_AWD_SCB_DSL_SynCOCO2LIS.py
mask_rcnn_convnext-t_p4_w7_fpn_AWD_SCB_DSL_SynCOCO2LIS=36.8.pth	614M	36.8 / 42.7	36.8 / 42.7 ✅	Configs/mask_rcnn_convnext-t_p4_w7_fpn_AWD_SCB_DSL_SynCOCO2LIS.py
pointrend_r50_fpn_caffe_AWD_SCB_DSL_SynCOCO2LIS=32.8.pth	466M	32.8 / 37.1	32.8 / 37.1 ✅	Configs/pointrend_r50_fpn_caffe_AWD_SCB_DSL_SynCOCO2LIS.py
mask2former_r50_AWD_SCB_DSL_SynCOCO2LIS=37.8.pth	554M	35.6 / 37.8	35.5 / 37.8 ✅	Configs/mask2former_r50_caffe_AWD_SCB_DSL_SynCOCO2LIS.py
mask_rcnn_r50_fpn_caffe_AWD_SCB_DSL_COCO80_1x_COCO=36.1.pth	372M	36.1 / 39.5 (COCO val2017, normal light)	36.1 / 39.5 ✅	Configs/mask_rcnn_r50_fpn_caffe_AWD_SCB_DSL_COCO80.py

Baselines / 基线（same detector, without our components / 同检测器去掉论文组件）

File	Size	Paper	Re-eval	Config (in repo)
mask_rcnn_r50_fpn_COCO8=19.8.pth	352M	19.8 / 22.8	19.8 / 22.8 ✅	Configs/mask_rcnn_r50_fpn_caffe_COCO2LIS.py
mask_rcnn_convnext-t_p4_w7_fpn_COCO2LIS=23.7.pth	573M	23.7 / 27.9	23.7 / 27.9 ✅	Configs/mask_rcnn_convnext-t_p4_w7_fpn_COCO2LIS.py
pointrend_r50_fpn_caffe_COCO2LIS=20.6.pth	449M	20.6 / 23.5	20.6 / 23.5 ✅	Configs/pointrend_r50_fpn_caffe_COCO2LIS.py
mask2former_r50_SynCOCO2LIS=21.4.pth	528M	21.4 / 22.9	21.4 / 22.9 ✅	Configs/mask2former_r50_COCO2LIS.py

MD5 checksums are provided in md5.txt. File sizes are decimal MB (e.g. 369M = 369 MB ≈ 352 MiB).

Configs/ 指向代码仓库中的 config 文件。mask2former 系的 config 文件名以实际仓库为准（mask2former_r50_caffe_* / mask2former_r50_*）。

Evaluation Protocols / 评测协议

Two test protocols are used by the two config families, exactly as in the paper:

• Ours 系列（png 协议）: lis_coco_png_test+1.json on RAW_Dark/ png images. The paper/re-eval numbers use the full 669 images / 2,934 instances; the released (privacy-filtered) json ships 604 images / 2,680 instances. Repo config paths expect data/LIS/ with the layout described in the code README.
• Baseline 系列（JPG 相机原图协议）: lis_coco_JPG_test+1.json on camera original .JPG files (RGB_Dark/ in the HF dataset).

论文与复评均在完整测试集（669 图）上进行；HF 数据集公开版过滤了 179 对隐私敏感图像，用公开版测试集复现会有细微数字差异（详见数据卡）。实测：R50 Ours 在公开版测试集（604 图）上 segm 32.0 / bbox 37.5，与论文数字基本一致。

⚠️ Class-name note / 类别名说明: the configs pair label ids with class names via the tuple (bicycle, car, motorbike, bus, bottle, chair, diningtable, tvmonitor), which matches the true id semantics of the dataset's 0-based category ids. The categories/name block inside the released json files is historically misaligned with these true semantics (verified instance-by-instance) — do not rename it, and see the dataset card's Category mapping section before any class-dependent post-processing. / config 通过元组 (bicycle, car, motorbike, bus, bottle, chair, diningtable, tvmonitor) 绑定标签与类名，该元组与数据集 0 起始类别 id 的真实语义一致；发布 json 内的 categories 名字块与真实语义存在历史错位（已逐类验证），请勿改名，做任何依赖类名的后处理前先阅读数据卡的 Category mapping 一节。

Usage / 用法

git clone https://github.com/Linwei-Chen/LIS && cd LIS
huggingface-cli download Linwei-Chen/LIS --repo-type dataset --local-dir data/LIS   # eval set
# or download only what evaluation needs (~1.6G instead of the full dataset):
# huggingface-cli download Linwei-Chen/LIS --repo-type dataset --local-dir data/LIS --include "RAW_Dark/*" "annotations/*"
huggingface-cli download Linwei-Chen/LIS-models mask_rcnn_r50_fpn_caffe_AWD_SCB_DSL_SynCOCO2LIS=31.8.pth --local-dir checkpoints
ln -sfn ../data mmdetection/data && cd mmdetection
python tools/test.py ../Configs/mask_rcnn_r50_fpn_caffe_AWD_SCB_DSL_SynCOCO2LIS.py \
    ../checkpoints/mask_rcnn_r50_fpn_caffe_AWD_SCB_DSL_SynCOCO2LIS=31.8.pth --eval bbox segm
# → segm mAP 32.0 / bbox mAP 37.5 on the privacy-filtered release test set (measured)
#   (paper: 31.8 / 37.6 on the full 669-image test set)

For inference-time speedup, tools/reparameter.py folds the adaptive weighted downsampling into a single convolution (see repo README). / tools/reparameter.py 可将自适应加权下采样折叠为单卷积以加速推理（见仓库 README）。

Environment: PyTorch 1.11.0 + cu113, mmcv-full 1.4.8, mmdet 2.15.1 (the repo's bundled fork).

License / 许可

Checkpoints are released under the MIT license, same as the code.

权重与代码同样以 MIT 许可 发布。

Citation / 引用

@article{2023lis,
  title={Instance Segmentation in the Dark},
  author={Chen, Linwei and Fu, Ying and Wei, Kaixuan and Zheng, Dezhi and Heide, Felix},
  journal={International Journal of Computer Vision},
  volume={131},
  number={8},
  pages={2198--2218},
  year={2023},
  publisher={Springer}
}