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ashiq24/
dinov2-base-lse

Name: dinov2-base-lse
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LSE-DINOv2: Local Scale Equivariant DINOv2

License

PyTorch

timm

A DINOv2 Vision Transformer equipped with Deep Equilibrium Model (DEM) based local scale adaptation for improved scale equivariance (local scale consistency).

🚀 Quick Start

Basic Inference

⚠️ Important: This model uses custom code. The example below automatically handles downloading the custom code files (modeling_lse_dinov2.py and configuration_lse_dinov2.py) to the cache using trust_remote_code=True and snapshot_download. Make sure you trust the source before running custom code.

python

import torch
import sys
from PIL import Image
from torchvision import transforms
from transformers import AutoConfig
from huggingface_hub import snapshot_download

# Helper function to load the model (handles custom code automatically)
def load_lse_dinov2(repo_name="ashiq24/dinov2-base-lse"):
    """Load LSE-DINOv2 model from HuggingFace Hub."""
    import sys
    from huggingface_hub import snapshot_download
    
    # Load config with trust_remote_code to download custom code
    config = AutoConfig.from_pretrained(repo_name, trust_remote_code=True)
    
    # Download model files to cache and get the directory
    cache_dir = snapshot_download(repo_id=repo_name, allow_patterns="*.py")
    
    # Add cache directory to Python path
    if cache_dir not in sys.path:
        sys.path.insert(0, cache_dir)
    
    # Import model class from downloaded files
    from modeling_lse_dinov2 import LSEDinoV2ForImageClassification
    return LSEDinoV2ForImageClassification.from_pretrained(repo_name)

# Load model
model = load_lse_dinov2("ashiq24/dinov2-base-lse")
model.eval()

# Move to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# Prepare image with ImageNet preprocessing
transform = transforms.Compose([
    transforms.Resize(256, interpolation=transforms.InterpolationMode.BICUBIC),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Load and preprocess image
image = Image.open("your_image.jpg").convert("RGB")
pixel_values = transform(image).unsqueeze(0).to(device)

# Run inference
with torch.no_grad():
    outputs = model(pixel_values)
    predicted_class = outputs.logits.argmax(-1).item()

print(f"Predicted class: {predicted_class}")

🔑 Key Features

Learned Local Scaling: Learns to adaptively scale different image regions based on content
Deep Equilibrium Model: Uses fixed-point iteration to find optimal deformation parameters
Multi-Layer Adaptation: Applies scaling at multiple transformer layers
ImageNet-1K Trained: Fine-tuned on ImageNet-1k for classification

📦 Installation

bash

pip install torch torchvision timm transformers safetensors
pip install torchdeq  # Optional but Recommended: for DEQ solver, falls back to simple iteration if not installed. 

Deformation Visualization

The model learns content-aware local scaling transformations (canonicalization). Here's a visualization showing original images and their deformed versions at different layers:

Each row shows an input image and its learned deformations at the input, Layer 4, and Layer 8, along with the deformation magnitude heatmaps.

Extracting Learned Deformation Parameters

The model learns content-aware deformation parameters (phi) that can be obtained:

python

# Get phi parameters for visualization
with torch.no_grad():
    phi_x_list, phi_y_list = model.get_phi_parameters(pixel_values)

for i, (phi_x, phi_y) in enumerate(zip(phi_x_list, phi_y_list)):
    print(f"Layer {i}: phi_x shape {phi_x.shape}, phi_y shape {phi_y.shape}")

🏗️ Architecture

Base Model

Component	Value
Architecture	DINOv2 ViT-Base
Patch Size	14×14
Image Size	224×224
Embedding Dim	768
Transformer Blocks	12
Attention Heads	12
Register Tokens	4

DEM Adapter

Component	Value
Phi Layers	3
Local Scaling Grids	[16x16, 8x8, 8x8]
DEQ #Layers	2
CNN #Channels	[3, 64, 128]
DEQ Iterations	5
Applied at Layers	[input, 4, 8]

📊 Additional Resources

For detailed information about the model architecture, training procedures, and experimental results, please refer to:

GitHub Repository: local-scale-equivariance - Full codebase, training scripts, and documentation
Project Website: Local Scale Equivariance - visualizations, and additional resources
Paper: Local Scale Equivariance with Latent Deep Equilibrium Canonicalizer - Paper

📈 Training Details

Parameter	Value
Dataset	ImageNet-1K
Optimizer	AdamW
Backbone LR	5e-5
DEM LR	1e-5
Epochs	20
Batch Size	200

⚠️ Limitations

Computational Overhead: Additional inference time due to DEQ iterations
Memory Usage: Slightly higher than standard DINOv2
Dependencies: Requires timm and optionally torchdeq

📄 License

Apache 2.0

🙏 Acknowledgements

DINOv2 by Meta AI
timm by Ross Wightman
torchdeq for Deep Equilibrium Model implementation

📚 Citation

bibtex

@inproceedings{rahman2025local,
  title={Local Scale Equivariance with Latent Deep Equilibrium Canonicalizer},
  author={Rahman, Md Ashiqur and Yang, Chiao-An and Cheng, Michael N and Hao, Lim Jun and Jiang, Jeremiah and Lim, Teck-Yian and Yeh, Raymond A},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={10527--10537},
  year={2025}
}