README.md

---
license: other
license_name: dinov3-license
license_link: https://ai.meta.com/resources/models-and-libraries/dinov3-license
base_model: facebook/dinov3-vits16-pretrain-lvd1689m
tags:
- image-classification
- face-detection
- pytorch
- dinov3
---

# DINOv3 Face-Region Classifier

A binary face / non-face classifier finetuned from
[`facebook/dinov3-vits16-pretrain-lvd1689m`](https://huggingface.co/facebook/dinov3-vits16-pretrain-lvd1689m)
(DINOv3 ViT-S/16) with a single linear head on the pooled embedding. Given a `224x224` crop, it predicts
whether that crop contains a face — including non-human, pareidolia-style "faces" (e.g. two dots and a line
in tree bark or rock formations) — making it useful as a guidance signal for steering image generation
toward face-like content in a specific image region.

## Training data

- **ExpWild** and **iCartoonFace**: real human/cartoon face crops
- **FacesInThings**: pareidolia face crops (faces seen in inanimate objects/textures)
- Negatives: same-size random crops from the same images, sampled to not overlap any labeled face box

See [FacePareidolia/classifier](https://github.com/harveymannering/FacePareidolia/tree/main/classifier) for the
full training code (`train.py`, `face_dataset.py`).

## Metrics

Best checkpoint (epoch 4/20): **val_loss = 0.061, val_acc = 98.0%** (10% image-level holdout).

## Usage

The base DINOv3 checkpoint is gated on Hugging Face, so this repo ships its own `config.json`
(architecture metadata only — no gated weights) plus the finetuned weights, letting you rebuild the model
**without** requesting DINOv3 access:

```python
import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModel
from huggingface_hub import hf_hub_download

REPO_ID = "harveymannering/DINOv3_FaceRegionClassifier"

class Dinov3FaceRegionClassifier(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.backbone = AutoModel.from_config(config)
        self.head = nn.Linear(config.hidden_size, 1)

    def forward(self, pixel_values):
        pooled = self.backbone(pixel_values=pixel_values).pooler_output
        return self.head(pooled).squeeze(-1)  # face logit; sigmoid -> p(face)

config = AutoConfig.from_pretrained(REPO_ID)
model = Dinov3FaceRegionClassifier(config)
weights_path = hf_hub_download(repo_id=REPO_ID, filename="dinov3_face_region_classifier.pth")
model.load_state_dict(torch.load(weights_path, map_location="cpu"))
model.eval()
```

Input should be `224x224` RGB, normalized with ImageNet mean/std
(`mean=[0.485, 0.456, 0.406]`, `std=[0.229, 0.224, 0.225]`).

## License

Derived from `facebook/dinov3-vits16-pretrain-lvd1689m`; usage is subject to the terms of that model's
license — see the [base model page](https://huggingface.co/facebook/dinov3-vits16-pretrain-lvd1689m) for
details.