---
license: apache-2.0
library_name: transformers
base_model: []
tags:
  - gaze-estimation
  - gaze-target-estimation
  - dinov3
pipeline_tag: image-feature-extraction
---

# PaGE-ViT-B

Base variant, general scenes. Distilled. Part of the [PaGE](https://huggingface.co/Octopus1/PaGE) gaze target estimation family.

- **Backbone:** DINOv3 ViT-B/16
- **Params:** ~90M
- **Scene input:** 512×512, **Head input:** 256×256, **Heatmap output:** 64×64
- **Source checkpoint:** `vitb_distill.pt`

## Self-contained weights

This checkpoint includes the full DINOv3 backbone weights in its `safetensors` files. **No external
DINOv3 weights are downloaded.** The model code (`modeling_page.py`) is loaded automatically from
[`Octopus1/PaGE`](https://huggingface.co/Octopus1/PaGE) via `auto_map` when you pass `trust_remote_code=True`.

## Installation

```bash
pip install torch torchvision timm "transformers>=4.56" safetensors pillow
```

## Usage

```python
from transformers import AutoModel, AutoImageProcessor
from PIL import Image
import torch

repo = "Octopus1/page-vitb"
model = AutoModel.from_pretrained(repo, trust_remote_code=True).eval()
processor = AutoImageProcessor.from_pretrained(repo, trust_remote_code=True)

scene = Image.open("scene.jpg").convert("RGB")
head = Image.open("head.jpg").convert("RGB")

inputs = processor(scene, head_crops=[head], bboxes=[[(0.10, 0.10, 0.30, 0.40)]])

with torch.no_grad():
    out = model(inputs)

heatmap = out["heatmap"][0]   # [Np, 64, 64]
inout   = out["inout"][0]     # [Np]
```

## Inputs / Outputs

See the family [README](https://huggingface.co/Octopus1/PaGE) for the full input/output spec.

- Input dict: `images` (list of `[B,3,512,512]`), `head_images` (list of `[sum(Np),3,256,256]`),
  `bboxes` (per-image list of `(xmin,ymin,xmax,ymax)` in `[0,1]`).
- Output dict: `heatmap` (list of `[Np,64,64]`, sigmoid), `inout` (list of `[Np]`, sigmoid).

## License

Apache-2.0.