README.md

---
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},
}
```