add-teaser

BiRefNet_config.py

@@ -5,9 +5,7 @@ class BiRefNetConfig(PretrainedConfig):
     def __init__(
         self,
         bb_pretrained=False,
-        is_encoder_decoder=False,
         **kwargs
     ):
         self.bb_pretrained = bb_pretrained
-        self.is_encoder_decoder = is_encoder_decoder
         super().__init__(**kwargs)

README.md

@@ -1,25 +1,24 @@
 ---
-language: en
 license: apache-2.0
-pipeline_tag: image-segmentation
-library_name: transformers
+language: en
 tags:
-- layer decomposition
-- image segmentation
-- image matting
-- design
+  - layer decomposition
+  - image segmentation
+  - image matting
+  - design
+pipeline_tag: image-segmentation
 ---
 
 ## LayerD BiRefNet Matting Module
 
 <div align="left">
 
-[![arxiv paper](https://img.shields.io/badge/arxiv-paper-orange)](https://arxiv.org/abs/2509.25134)
+[![arxiv paper](https://img.shields.io/badge/arxiv-paper-orange)](https://arxiv.org/abs/)
 <a href='https://cyberagentailab.github.io/LayerD/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
 
 </div>
 
-This repository contains the code and model weights for the matting module in [LayerD [ICCV'25]](https://arxiv.org/abs/2509.25134), a layer decomposition framework for graphic design images.
+This repository contains the code and model weights for the matting module in [LayerD [ICCV'25]](https://arxiv.org/abs/), a layer decomposition framework for graphic design images.
 The model in this repository is **intended to be used as a part of the original [LayerD github repository](https://github.com/CyberAgentAILab/LayerD)**.
 Please visit https://github.com/CyberAgentAILab/LayerD for more information.
 
@@ -52,4 +51,4 @@ The original BiRefNet is released under the [MIT license](https://huggingface.co
   booktitle={ICCV},
   year={2025}
 }
-```
+```

birefnet.py

@@ -7,8 +7,6 @@ from transformers import PretrainedConfig
 
 class Config(PretrainedConfig):
     def __init__(self) -> None:
-        self.is_encoder_decoder = False
-        
         # PATH settings
         self.sys_home_dir = os.path.expanduser('~')     # Make up your file system as: SYS_HOME_DIR/codes/dis/BiRefNet, SYS_HOME_DIR/datasets/dis/xx, SYS_HOME_DIR/weights/xx
 
@@ -55,7 +53,7 @@ class Config(PretrainedConfig):
             }[self.task]
         ][1]    # choose 0 to skip
         self.lr = (1e-4 if 'DIS5K' in self.task else 1e-5) * math.sqrt(self.batch_size / 4)     # DIS needs high lr to converge faster. Adapt the lr linearly
-        self.size = 1024
+        self.size = 512
         self.num_workers = max(4, self.batch_size)          # will be decrease to min(it, batch_size) at the initialization of the data_loader
 
         # Backbone settings

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:28f8acba2736067bf2eb8152f2d3ce75a388dd4e4e57f068b30760a8fe1c44d0
+oid sha256:28416acdaedc3fbee0f80f888893a2fdca60ae81b1b27a41433a4e791598e330
 size 884878824