semseg

Depth Anything for Semantic Segmentation

We use our Depth Anything pre-trained ViT-L encoder to fine-tune downstream semantic segmentation models.

Performance

Cityscapes

Note that our results are obtained without Mapillary pre-training.

Method	Encoder	mIoU (s.s.)	m.s.
SegFormer	MiT-B5	82.4	84.0
Mask2Former	Swin-L	83.3	84.3
OneFormer	Swin-L	83.0	84.4
OneFormer	ConNeXt-XL	83.6	84.6
DDP	ConNeXt-L	83.2	83.9
Ours	ViT-L	84.8	86.2

ADE20K

Method	Encoder	mIoU
SegFormer	MiT-B5	51.0
Mask2Former	Swin-L	56.4
UperNet	BEiT-L	56.3
ViT-Adapter	BEiT-L	58.3
OneFormer	Swin-L	57.4
OneFormer	ConNeXt-XL	57.4
Ours	ViT-L	59.4

Pre-trained models

• Cityscapes-ViT-L-mIoU-86.4
• ADE20K-ViT-L-mIoU-59.4

Installation

Please refer to MMSegmentation for instructions.

After installation:

• move our config/depth_anything to mmseg's config
• move our dinov2.py to mmseg's backbones
• add DINOv2 in mmseg's models/backbones/__init__.py
• download our provided torchhub directory and put it at the root of your working directory

For training or inference with our pre-trained models, please refer to MMSegmentation instructions.