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README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+  - feature-upsampling
+  - pixel-dense-features
+  - computer-vision
+  - dinov3
+  - vision-transformer
+  - uplift
+datasets:
+  - ILSVRC/imagenet-1k
+---
+
+# UPLiFT for DINOv3-S+/16
+
+| Input Image | Base DINOv3 Features | UPLiFT Upsampled Features |
+|:-----------:|:--------------------:|:-------------------------:|
+| ![Input](Gigi_1_512.png) | ![Base Features](Gigi_1_512.png_uplift_dinov3-splus16-base-feature-PCA.png) | ![UPLiFT Features](Gigi_1_512.png_uplift_dinov3-splus16-4-PCA.png) |
+
+This is the official pretrained **UPLiFT** (Efficient Pixel-Dense Feature Upsampling with Local Attenders) model for the **DINOv3-S+/16** backbone.
+
+UPLiFT is a lightweight method to upscale features from pretrained vision backbones to create pixel-dense feature maps. It uses Local Attenders to efficiently upsample low-resolution backbone features while preserving semantic information.
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| **Backbone** | DINOv3-S+/16 (`vit_small_plus_patch16_dinov3.lvd1689m`) |
+| **Backbone Channels** | 384 |
+| **Patch Size** | 16 |
+| **Upsampling Factor** | 2x per iteration |
+| **Local Attender Size** | N=17 |
+| **Training Dataset** | ImageNet |
+| **Training Image Size** | 448x448 |
+| **License** | MIT |
+
+## Links
+
+- **Paper**: [Coming Soon]
+- **GitHub**: [https://github.com/mwalmer-umd/UPLiFT](https://github.com/mwalmer-umd/UPLiFT)
+- **Project Website**: [https://www.cs.umd.edu/~mwalmer/uplift/](https://www.cs.umd.edu/~mwalmer/uplift/)
+
+## Installation
+
+```bash
+pip install 'uplift[vit] @ git+https://github.com/mwalmer-umd/UPLiFT.git'
+```
+
+## Quick Start
+
+```python
+import torch
+from PIL import Image
+
+# Load model (weights auto-download from HuggingFace)
+model = torch.hub.load('mwalmer-umd/UPLiFT', 'uplift_dinov3_splus16')
+
+# Run inference
+image = Image.open('your_image.jpg')
+features = model(image)  # Returns pixel-dense features
+```
+
+## Usage Options
+
+### Adjust Upsampling Iterations
+
+Control the number of iterative upsampling steps (default: 4):
+
+```python
+# Fewer iterations = lower memory usage
+model = torch.hub.load('mwalmer-umd/UPLiFT', 'uplift_dinov3_splus16', iters=4)
+```
+
+### Raw UPLiFT Model (Without Backbone)
+
+Load only the UPLiFT upsampling module without the DINOv3 backbone:
+
+```python
+model = torch.hub.load('mwalmer-umd/UPLiFT', 'uplift_dinov3_splus16',
+                       include_extractor=False)
+```
+
+### Return Base Features
+
+Get both upsampled and original backbone features:
+
+```python
+model = torch.hub.load('mwalmer-umd/UPLiFT', 'uplift_dinov3_splus16',
+                       return_base_feat=True)
+upsampled_features, base_features = model(image)
+```
+
+## Architecture
+
+UPLiFT consists of:
+
+1. **Encoder**: Processes the input image with a series of convolutional blocks to create dense representations to guide feature upsampling
+2. **Decoder**: Upsamples features using transposed convolutions with bilinear residual connections
+3. **Local Attender**: A local-neighborhood-based attention pooling module that maintains semantic consistency with the original features
+
+The model uses encoder sharing, meaning a single encoder pass is used across all upsampling iterations for efficiency.
+
+## Intended Use
+
+This model is designed for:
+
+- Creating pixel-dense feature maps from DINOv3 features
+- Dense prediction tasks (semantic segmentation, depth estimation, etc.)
+- Feature visualization and analysis
+- Research on vision foundation models
+
+## Limitations
+
+- Optimized specifically for DINOv3-S+/16 features; may not generalize to other backbones without retraining
+- Performance depends on the quality of the underlying DINOv3 features
+- Higher iteration counts increase computation time
+
+## Citation
+
+If you use UPLiFT in your research, please cite our paper.
+
+[citation coming soon]
+
+## Acknowledgements
+
+This work builds upon:
+- [DINOv3](https://github.com/facebookresearch/dinov3) by Meta AI
+- [timm](https://github.com/huggingface/pytorch-image-models) for model loading

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