README.md

metadata

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

---

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)

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

# 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

@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},
}