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aadex/
upernet-rope-vit-tiny-512x512-ade20k

Name: upernet-rope-vit-tiny-512x512-ade20k
Availability: InStock

mmsegmentation

semantic-segmentation

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UPerNet with RoPE-ViT Backbone for Semantic Segmentation

This model is a UPerNet semantic segmentation model with a RoPE-ViT (Vision Transformer with Rotary Position Embeddings) backbone, trained on the ADE20K dataset.

Model Description

Architecture: UPerNet
Backbone: RoPE-ViT Tiny
Dataset: ADE20K
Task: Semantic Segmentation
Framework: MMSegmentation

Training Results

Metric	Value
mIoU	24.94%
mAcc	33.24%
aAcc	72.18%

Usage

python

from mmseg.apis import init_model, inference_model

config_file = 'upernet_rope_vit_tiny_512x512_ade20k.py'
checkpoint_file = 'best_mIoU_iter_40000.pth'

# Initialize the model
model = init_model(config_file, checkpoint_file, device='cuda:0')

# Inference on an image
result = inference_model(model, 'demo.jpg')

Training Configuration

The model was trained with the following configuration:

Input size: 512x512
Training iterations: 40,000
Optimizer: AdamW
Learning rate scheduler: Polynomial decay

Citation

If you use this model, please cite:

bibtex

@misc{rope-vit-segmentation,
  author = {VLG IITR},
  title = {UPerNet with RoPE-ViT for Semantic Segmentation},
  year = {2026},
  publisher = {Hugging Face},
}

License

This model is released under the Apache 2.0 license.

aadex/upernet-rope-vit-tiny-512x512-ade20k