softeq-dinov2-base-ade20k-seg-c4-s0.8-sp1.0

Tunable Soft Equivariance with Guarantees

Paper: Tunable Soft Equivariance with Guarantees
Authors: Md Ashiqur Rahman, Lim Jun Hao, Jeremiah Jiang, Teck-Yian Lim, Raymond A. Yeh

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Overview

This repository hosts soft-equivariant vision models introduced in our paper. This repository contains a soft-equivariant VIT fine-tuned on ADE20k. It uses a linear segmentation head.

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Usage

Note: All models require trust_remote_code=True because they use custom model classes.

Semantic Segmentation (ViT backbone)

from transformers import AutoModel, AutoConfig
import torch
import torch.nn.functional as F

model_id = "ashiq24/softeq-dinov2-base-ade20k-seg-c4-s0.8-sp1.0"

config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
model  = AutoModel.from_pretrained(model_id, trust_remote_code=True)
model.eval()

# Input size must match model training resolution (e.g., 512×512)
pixel_values = torch.randn(1, 3, 512, 512)

with torch.no_grad():
    outputs = model(pixel_values=pixel_values)

# outputs.logits shape: (1, num_labels, H, W) — already upsampled to input resolution
seg_map = outputs.logits.argmax(dim=1)   # (1, H, W) predicted label per pixel

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Configuration Parameters

The SoftEqConfig class stores all architectural parameters. Key fields:

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Citation

If you use these models in your research, please cite:

@article{rahman2026tunable,
  title={Tunable Soft Equivariance with Guarantees},
  author={Rahman, Md Ashiqur and Hao, Lim Jun and Jiang, Jeremiah and Lim, Teck-Yian and Yeh, Raymond A},
  journal={arXiv preprint arXiv:2603.26657},
  year={2026}
}