EG-SAM: An Edge-Guided SAM for Accurate Complex Object Segmentation

Pipeline

Environment

Python 3.8

CUDA 11.7

PyTorch 1.13.1

TorchVision 0.14.1

Datasets

We follow the data set structure of HQSeg-44K as follows：

data
|____DIS5K
|____cascade_psp
| |____DUTS-TE
| |____DUTS-TR
| |____ecssd
| |____fss_all
| |____MSRA_10K
|____thin_object_detection
| |____COIFT
| |____ThinObject5K

You can get the datasets from here

Train

python -m torch.distributed.launch --nproc_per_node=<num_gpus> train.py --checkpoint <your checkpoint path> --model-type <model_type> --output <your output path>

EG-SAM is an improvement on the basis of HQ-SAM, you can follow the environment Settings here

Evaluation

python -m torch.distributed.launch --nproc_per_node=<num_gpus> train.py --checkpoint <your checkpoint path> --model-type <model_type> --output <your output path> --eval --restore-model <your training_checkpoint path>

You can get the weight file here

Visualization

Visual comparison with nine state-of-the-art COD methods. EG-SAM demonstrates superior accuracy in delineating the boundaries of camouflaged objects.

Results on DIS，COIFT and ThinObject

Results on CODs