Code of paper A new baseline for edge detection: Make Encoder-Decoder great again

pip install -r requirements.txt

Modify the values of ckpt and img in inference.py. ckpt on BSDS can be found from https://drive.google.com/file/d/1TKg37m3KWuv4A8FTlXJ-N20Ar46PmRdH/view?usp=sharing And running command

python inference.py

Download the dataset to any dir and point to the dir in the code
-BSDS500 following the setting of "The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector"
-NYUDv2 following the setting of "Pixel Difference Networks for Efficient Edge Detection" and random crop to 400*400
-BIPED following the setting of "Dense Extreme Inception Network for Edge Detection"

Down it from https://huggingface.co/sail/dl/resolve/main/caformer/caformer_m36_384_in21ft1k.pth and put it into the dir ./model

python main.py --batch_size 4 --stepsize 3-4 --gpu 1 --savedir 0305-bsds --encoder Dul-M36 --decoder unetp --head default --note 'training on BSDS500' --dataset BSDS --maxepoch 6

Following the previous methods. such as RCF and PiDiNet

The result of BSDS500 can be download here https://drive.google.com/file/d/1PiPklsH7w6zNxdGWW-JpnUsFOdiYLHwG/view?usp=sharing

checkpoint on BIPED is here

ODS/OIS on BIPED is a little error, due to a wrong tolerance is used. And it is corrected in version 2 Thanks for the reminder from yx-yyds

Main features: By introducing Evidence Lower Bound loss and learnable Gaussian distributions, DDN is capable of generating multi-granularity edges. The ODS of DDN on the BSDS500 dataset is 0.867, which is 0.022 higher than that of NBED (0.845)