Upload DINOv3 face-region classifier (finetuned, epoch 4, val_acc 98.0%)

README.md

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+---
+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.

config.json

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+{
+  "architectures": [
+    "DINOv3ViTModel"
+  ],
+  "attention_dropout": 0.0,
+  "drop_path_rate": 0.0,
+  "dtype": "float32",
+  "hidden_act": "gelu",
+  "hidden_size": 384,
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "key_bias": false,
+  "layer_norm_eps": 1e-05,
+  "layerscale_value": 1.0,
+  "mlp_bias": true,
+  "model_type": "dinov3_vit",
+  "num_attention_heads": 6,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "num_register_tokens": 4,
+  "patch_size": 16,
+  "pos_embed_jitter": null,
+  "pos_embed_rescale": 2.0,
+  "pos_embed_shift": null,
+  "proj_bias": true,
+  "query_bias": true,
+  "rope_theta": 100.0,
+  "transformers_version": "4.56.1",
+  "use_gated_mlp": false,
+  "value_bias": true
+}

dinov3_face_region_classifier.pth

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+size 86459431