Update model card

README.md

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+---
+license: apache-2.0
+language:
+  - en
+tags:
+  - scene-graph-generation
+  - object-detection
+  - visual-relationship-detection
+  - pytorch
+  - yolo
+pipeline_tag: object-detection
+library_name: sgg-benchmark
+model-index:
+  - name: REACT++ yolov8m
+    results:
+      - task:
+          type: object-detection
+          name: Scene Graph Detection
+        dataset:
+          name: IndoorVG
+          type: indoorvg
+        metrics: []
+---
+
+# REACT++ Scene Graph Generation — IndoorVG (yolov8m)
+
+This repository contains **REACT++** model checkpoints for scene graph generation (SGG)
+on the **IndoorVG** benchmark, across 1 backbone size.
+
+REACT++ is a parameter-efficient, attention-augmented relation predictor built on top of
+a YOLO backbone.  It uses:
+
+- **DAMP** (Detection-Anchored Multi-Scale Pooling), a new simple pooling algorithm for one-stage object detectors such as YOLO
+- **SwiGLU gated MLP** for all feed-forward blocks (½ the params of ReLU-MLP at equal capacity)
+- **Visual x Semantic cross-attention** — visual tokens attend to GloVe prototype embeddings
+- **Geometry RoPE** — box-position encoded as a rotary frequency bias on the Q matrix
+- **Prototype Momentum Buffer** — per-class EMA prototype bank
+- **P5 Scene Context** — AIFI-enhanced P5 tokens provide global context via cross-attention
+
+The models were trained with the
+[SGG-Benchmark](https://github.com/Maelic/SGG-Benchmark) framework and described in the
+[REACT++ paper (Neau et al., 2026)](https://arxiv.org/abs/2603.06386).
+
+---
+
+## Results — SGDet on IndoorVG test split (ONNX, CUDA)
+
+> Metrics from end-to-end ONNX evaluation (`tools/eval_onnx_psg.py`). E2E Latency = image load + pre-process + ONNX forward.
+
+| Backbone | Params | R@20 | R@50 | R@100 | mR@20 | mR@50 | mR@100 | F1@20 | F1@50 | F1@100 | E2E Lat. (ms) |
+|----------|:------:|-----:|-----:|------:|------:|------:|-------:|------:|------:|-------:|--------------:|
+| yolov8m | ~25.9M | - | - | - | - | - | - | - | - | - | - |
+
+---
+
+## Checkpoints
+
+| Variant | Sub-folder | Checkpoint files |
+|---------|------------|-----------------|
+| yolov8m | `yolov8m/` | `yolov8m/model.onnx` (ONNX) · `yolov8m/best_model_epoch_8.pth` (PyTorch) |
+
+---
+
+## Usage
+
+### ONNX (recommended — no Python dependencies beyond onnxruntime)
+
+```python
+from huggingface_hub import hf_hub_download
+
+onnx_path = hf_hub_download(
+    repo_id="maelic/REACTPlusPlus_IndoorVG",
+    filename="yolov8m/react_pp_yolo12m.onnx",
+    repo_type="model",
+)
+# Run with tools/eval_onnx_psg.py or load directly via onnxruntime
+```
+
+### PyTorch
+
+```python
+# 1. Clone the repository
+#    git clone https://github.com/Maelic/SGG-Benchmark
+
+# 2. Install dependencies
+#    pip install -e .
+
+# 3. Download checkpoint + config
+from huggingface_hub import hf_hub_download
+
+ckpt_path = hf_hub_download(
+    repo_id="maelic/REACTPlusPlus_IndoorVG",
+    filename="yolov8m/best_model.pth",
+    repo_type="model",
+)
+cfg_path = hf_hub_download(
+    repo_id="maelic/REACTPlusPlus_IndoorVG",
+    filename="yolov8m/config.yml",
+    repo_type="model",
+)
+
+# 4. Run evaluation
+import subprocess
+subprocess.run([
+    "python", "tools/relation_eval_hydra.py",
+    "--config-path", str(cfg_path),
+    "--task", "sgdet",
+    "--eval-only",
+    "--checkpoint", str(ckpt_path),
+])
+```
+
+---
+
+## Citation
+
+```bibtex
+@article{neau2026reactpp,
+  title   = {REACT++: Efficient Cross-Attention for Real-Time Scene Graph Generation
+},
+  author  = {Neau, Maëlic and Falomir, Zoe},
+  year    = {2026},
+  url     = {https://arxiv.org/abs/2603.06386},
+}
+```