---
license: mit
language:
  - en
tags:
  - vision
  - image-segmentation
  - image-feature-extraction
  - region-tokens
  - dinov3
  - pytorch
library_name: transformers
---

# T-REN: Text-Aligned Region Encoder Network

**Authors**: [Savya Khosla](https://savya08.github.io/), [Sethuraman TV](https://github.com/sethuramanio), [Aryan Chadha](https://www.linkedin.com/in/aryan-chadha/), [Alex Schwing](https://www.alexander-schwing.de/), [Derek Hoiem](https://dhoiem.cs.illinois.edu/)

[![GitHub](https://img.shields.io/badge/GitHub-Code-black.svg)](https://github.com/savya08/T-REN)

T-REN (**T**ext-aligned **R**egion **E**ncoder **N**etwork) is an image encoder that produces region-level tokens aligned with text, built on top of [DINOv3](https://github.com/facebookresearch/dinov3) ViT-L/16. Compared to its patch-based backbone, T-REN delivers:

- **+5.9 mIoU** on ADE20K open-vocabulary segmentation
- **+18.4% recall** on COCO object-level text-image retrieval
- **+15.6% recall** on Ego4D video object localization (VQ2D)
- **+17.6% mIoU** on VSPW video scene parsing
- **24× fewer tokens** per image, **187× fewer** per video

---

## What's in this repo

This HuggingFace repo contains:
- `model.safetensors` — the trained `RegionEncoder` head weights (~1.2 GB)
- `configuration_tren.py`, `modeling_tren.py`, `model.py`, `task_utils.py` — source code for `trust_remote_code`

**The DINOv3 ViT-L/16 backbone is NOT included here** — it belongs to Facebook Research and must be obtained separately (see below).

---

## Quickstart

### Step 1 — Install dependencies

```bash
pip install transformers torch torchvision kornia
```

### Step 2 — Get the DINOv3 weights

T-REN's backbone is DINOv3 ViT-L/16 with a DINOtxt text-alignment head. You need two weight files from the [DINOv3 release](https://github.com/facebookresearch/dinov3):

| File | Description |
|------|-------------|
| `dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth` | DINOv3 ViT-L/16 backbone |
| `dinov3_vitl16_dinotxt_vision_head_and_text_encoder-a442d8f5.pth` | DINOtxt vision head + text encoder |

Place both files in the same directory, e.g. `/path/to/dinov3_weights/`.

### Step 3 — Load and run T-REN

```python
import torch
import torchvision.transforms as T
from PIL import Image
from transformers import AutoModel

# Load model (downloads T-REN weights from this repo automatically)
model = AutoModel.from_pretrained("aryaaan12/T-REN", trust_remote_code=True)

# Load the DINOv3 backbone from your local directory
model.load_backbone("/path/to/dinov3_weights/")

model.eval()

# Prepare an image — resize to 512x512, values in [0, 1]
transform = T.Compose([
    T.Resize((512, 512)),
    T.ToTensor(),
])
image = transform(Image.open("your_image.jpg").convert("RGB"))
image = image.unsqueeze(0)  # (1, 3, 512, 512)

# Run T-REN
with torch.no_grad():
    outputs = model(image)

# Outputs
region_tokens = outputs["text_aligned_tokens"]  # list of (N, 1024) per image
region_masks  = outputs["region_masks"]          # list of (N, 32, 32) per image
class_token   = outputs["class_tokens"]          # (1, 1024) image-level token
print(f"Number of region tokens: {len(region_tokens[0])}")
```

### Text-guided region matching

```python
import torch.nn.functional as F

texts = ["sky", "car", "building", "tree", "road"]

with torch.no_grad():
    outputs = model(image, texts=texts)

region_tokens = outputs["text_aligned_tokens"][0]   # (N, 1024)
text_tokens   = outputs["text_encodings"]           # (5, 1024)

# Cosine similarity: which text label fits each region best?
sim = F.normalize(region_tokens, dim=-1) @ F.normalize(text_tokens, dim=-1).T
best_labels = sim.argmax(dim=-1)
print([texts[i] for i in best_labels])
```

---

## Model details

| | |
|---|---|
| Architecture | RegionEncoder (cross-attention decoder) over DINOv3 ViT-L/16 features |
| Trainable parameters | 31.5M (RegionEncoder head only; backbone is frozen) |
| Input resolution | 512 × 512 |
| Output token dim | 1024 |
| Multiscale regions | 3 scales per prompt point |
| Text embedding space | DINOtxt (aligned with DINOv3 text encoder) |

---

## Citation

```bibtex
@misc{khosla2026tren,
      title={T-REN: Learning Text-Aligned Region Tokens Improves Dense Vision-Language Alignment and Scalability},
      author={Savya Khosla and Sethuraman T V and Aryan Chadha and Alexander Schwing and Derek Hoiem},
      year={2026},
}
```