同步操作将从 恍兮惚兮/DDRNet.pytorch 强制同步,此操作会覆盖自 Fork 仓库以来所做的任何修改,且无法恢复!!!
确定后同步将在后台操作,完成时将刷新页面,请耐心等待。
This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data!on single 2080Ti GPU, DDRNet-23-slim yields 77.4% mIoU at 109 FPS on Cityscapes test set and 74.4% mIoU at 230 FPS on CamVid test set.
The code mainly borrows from HRNet-Semantic-Segmentation OCR and the official repository, thanks for their work.
类别 | 标签 | 像素类 |
---|---|---|
实线 | S0001 | 1 |
虚线 | S0002 | 2 |
提示前方直行 | S0075 | 3 |
提示前方左转 | S0077 | 4 |
提示前方右转 | S0078 | 5 |
提示前方可直行或左转 | S0076 | 6 |
提示前方可直行或右转 | S0079 | 7 |
提示直行或左转或右转 | S0106 | 8 |
提示前方左转或右转 | S0083 | 9 |
提示前方掉头 | S0080 | 10 |
提示前方有左弯或需向左合流 | S0084 | 11 |
提示前方有右弯或需向右合流 | S0085 | 12 |
人行横道 | S0088 | 13 |
停止线(单实线) | S0089 | 14 |
停车让行线(双实线) | S0090 | 15 |
减速让行线(双虚线) | S0091 | 16 |
减速带 | S0092 | 17 |
Here I list the software and hardware used in my experiment
You need to download the Cityscapesdatasets. and rename the folder cityscapes
, then put the data under data
folder.
└── data
├── cityscapes
└── list
download the pretrained model on imagenet or the segmentation model from the official,and put the files in ${PROJECT}/pretrained_models
folder
use the official pretrained model and our eval.py
code. with ydhongHIT's advice now can reach the same accuracy in the paper. Thanks.
cd ${PROJECT}
python tools/eval.py --cfg experiments/cityscapes/ddrnet23_slim.yaml
model | Train Set | Test Set | OHEM | Multi-scale | Flip | mIoU | Link |
---|---|---|---|---|---|---|---|
DDRNet23_slim | unknown | eval | Yes | No | No | 77.83 | official |
DDRNet23_slim | unknown | eval | Yes | No | Yes | 78.42 | official |
DDRNet23 | unknown | eval | Yes | No | No | 79.51 | official |
DDRNet23 | unknown | eval | Yes | No | Yes | 79.98 | official |
Note
ALIGN_CORNERS: false
in ***.yaml
will reach higher accuracy.download the imagenet pretrained model, and then train the model with 2 nvidia-3080
cd ${PROJECT}
python -m torch.distributed.launch --nproc_per_node=2 tools/train.py --cfg experiments/cityscapes/ddrnet23_slim.yaml
the own trained model coming soon
model | Train Set | Test Set | OHEM | Multi-scale | Flip | mIoU | Link |
---|---|---|---|---|---|---|---|
DDRNet23_slim | train | eval | Yes | No | Yes | 77.77 | Baidu/password:it2s |
DDRNet23_slim | train | eval | Yes | Yes | Yes | 79.57 | Baidu/password:it2s |
DDRNet23 | train | eval | Yes | No | Yes | ~ | None |
DDRNet39 | train | eval | Yes | No | Yes | ~ | None |
Note
ALIGN_CORNERS: true
in ***.yaml
, because i use the default setting in HRNet-Semantic-Segmentation OCR.align_corners=True
with better performance, the default option is False
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