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 framework and described in the REACT++ paper (Neau et al., 2026).

Results — SGDet on IndoorVG test split (CUDA, max_det=100, batch_size=1)

Metrics from end-to-end evaluation (tools/evaluate.py). Latency = model forward only.

Backbone	R@20	R@50	R@100	mR@20	mR@50	mR@100	F1@20	F1@50	F1@100	Lat. (ms)
yolov8m	23.1	30.27	35.1	17.54	22.3	24.97	19.94	25.68	29.18	19.1

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},
}