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README.md

@@ -1,128 +1,16 @@
 ---
-title: Age Estimation Demo
-emoji: 🚀
+title: FaceAge-DINOv3 Demo
+emoji: 🧑‍🦳
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-python_version: 3.12
+sdk_version: "4.0.0"
 app_file: app.py
-license: apache-2.0
-tags:
-  - age-estimation
-  - gender-classification
-  - face-analysis
-  - vision-transformer
-  - dinov3
-  - coral-ordinal-regression
-pipeline_tag: image-classification
+pinned: false
 ---
 
-# FaceAge-DINOv3
+# FaceAge-DINOv3 — Age & Gender Estimation Demo
 
-Age and gender estimation from face crops using **DINOv3-ViT-L** backbone with CORAL ordinal regression.
+Upload a photo to get age and gender predictions.
 
-## Performance (LAGENDA benchmark)
-
-| Model | MAE ↓ | CS@5 ↑ | Gender Acc ↑ |
-|-------|--------|--------|-------------|
-| MiVOLO v2 [face+body] (paper) | 3.650 | 74.48% | 97.99% |
-| MiVOLO v2 [face+body] (measured on the public model) | 3.859 | 76.5% | — |
-| MiVOLO v2 [face-only] (measured on the public model) | 3.941 | 75.6% | — |
-| **FaceAge-DINOv3 (face-only)** | **3.760** | — | — |
-
-Trained on:Our collection data.
-
-## Architecture
-
-```
-Face [B, 3, 224, 224]
-    ↓
-DINOv3-ViT-L/16  (307M params, pretrained on LVD-1.68B)
-    ↓ pooler_output
-[B, 1024]
-    ↓ LayerNorm → Linear(1024→512) → GELU → Dropout
-[B, 512]
-    ├── age_head:    Linear(512, 100) → CORAL → age ∈ [0, 100]
-    └── gender_head: Linear(512, 2)  → softmax → {female, male}
-```
-
-**CORAL ordinal regression**: age = Σ σ(logit_k) for k=0..99. Exploits the ordinal structure of ages (25 < 26 < 27) for better calibration than standard cross-entropy.
-
-## Usage
-
-```python
-from PIL import Image
-from transformers import AutoImageProcessor, AutoModel
-
-processor = AutoImageProcessor.from_pretrained("trungthanhtran/faceage-dino")
-model = AutoModel.from_pretrained("trungthanhtran/faceage-dino",
-                                   trust_remote_code=True)
-model.eval()
-
-# Input: 224×224 face crop (already cropped, no detection needed)
-image = Image.open("face_crop.jpg").convert("RGB")
-inputs = processor(images=image, return_tensors="pt")
-
-import torch
-with torch.no_grad():
-    outputs = model(**inputs)
-
-age    = outputs.age_output.item()
-gender = "male" if outputs.gender_class_idx.item() == 1 else "female"
-conf   = outputs.gender_probs[0, outputs.gender_class_idx.item()].item()
-
-print(f"Age    : {age:.1f}")
-print(f"Gender : {gender}  (conf={conf:.2f})")
-```
-
-## ONNX (no PyTorch needed)
-
-The model is also available as a single-file ONNX for CPU deployment:
-
-```bash
-pip install onnxruntime numpy pillow
-python infer_onnx.py --onnx faceage_dino_fp32.onnx --image face.jpg
-```
-
-ONNX is ~3-4× faster on CPU than the PyTorch model and requires no GPU.
-
-## Benchmark against MiVOLO v2
-
-```bash
-python infer_onnx.py \
-    --onnx   faceage_dino_fp32.onnx \
-    --lagenda_dir   data/lagenda \
-    --annotation_csv lagenda_test.csv \
-    --batch_size 256
-```
-
-## Training
-
-Multi-phase fine-tuning on DINOv3-ViT-L:
-
-| Phase | Backbone | LR | Data |
-|-------|----------|-----|------|
-| 1 | Frozen (all 24 blocks) | 1e-3 | Our collection  786k faces |
-| 2 | Top 4 blocks unfrozen | 1e-4 | Same |
-| 3 | All blocks unfrozen | 3e-5 | Same |
-| 4 | All blocks | 3e-6 | Our collection  4M faces, age reweighting |
-
-Age group reweighting (Phase 4): 36-50 ×2.0, 51-65 ×1.5, 66-100 ×3.0 to improve accuracy on older faces.
-
-## Citation
-
-If you use this model, please cite:
-
-```bibtex
-@misc{faceage-dino-2026,
-  title  = {FaceAge-DINOv3: Age and Gender Estimation with DINOv3-ViT-L},
-  author = {Trung Thanh Tran},
-  year   = {2026},
-  url    = {https://huggingface.co/trungthanhtran/faceage-dino}
-}
-```
-
-Also cite the backbones and datasets used:
-- DINOv3: Meta AI, "DINOv3: Scaling Up Vision Foundation Models", 2025
-- LAGENDA: Bhuiyan et al., 2023
-- MiVOLO: Kuprashevich & Tolstykh, arXiv 2307.04616
+Model: [TrungTran/faceage-dino](https://huggingface.co/TrungTran/faceage-dino)