FAQ

FAQ

General

What is FlashFusion?

FlashFusion is a multi-model vision fusion framework. It combines predictions from multiple vision models (detection, classification, segmentation) using configurable strategies like Weighted Box Fusion, Voting, and Cascade.

How is FlashFusion different from a simple ensemble?

FlashFusion provides:

• Multiple fusion strategies beyond simple NMS
• Multi-task support (det + cls + seg in one pipeline)
• Cascade pipelines for sequential refinement
• Consistency losses for training agreement between models
• Built-in benchmarking and profiling tools

Which fusion strategy should I use?

Scenario	Recommended Strategy
Multiple detectors, maximize mAP	WBF
Speed-critical with multiple detectors	NMS
Classification ensemble	Voting
Progressive refinement (coarse → fine)	Cascade
Learned combination with training data	Stacking

Performance

What FPS can I expect?

FPS depends on:

• Number of models in the ensemble
• Model sizes (S/M/L)
• Input resolution
• Hardware (GPU vs CPU)

Typical ranges on GPU:

• 2-model ensemble: 30-60 FPS
• 3-model ensemble: 20-40 FPS
• Cascade (2-stage): 25-50 FPS

How much does fusion improve accuracy?

Typical improvements over single-model:

• WBF ensemble (3 models): +2-5% mAP
• Cascade refinement: +1-3% mAP
• Multi-task fusion: task-dependent

Training

What gets trained in FlashFusion?

Base model backbones are frozen. Only fusion components are trained:

• Fusion heads/necks
• Learned strategy weights
• LoRA adapters (if enabled)

How much data do I need?

Since base models are frozen, fusion training is data-efficient:

• Minimum: ~500 annotated images
• Recommended: 2,000+ images
• Fine-tuning converges in 20-50 epochs typically

Can I use models from different frameworks?

Yes, as long as they're wrapped as PyTorch nn.Module instances with a forward method returning the expected dictionary format.

Deployment

Can I export to ONNX?

Yes:

flashfusion export --config configs/flashfusion_ensemble_320.yaml --format onnx

Or use the export example:

python examples/export_fused_model.py --output model.onnx

Does FlashFusion work on edge devices?

Yes, with optimizations:

• Use smaller base models (S variant)
• Reduce input resolution (160x160 or 224x224)
• Export to ONNX + TensorRT
• Use 2-model ensemble instead of 3+

Troubleshooting

Import errors

Run flashfusion check to verify all dependencies. Missing packages can be installed with:

pip install -e ".[all]"

Out of memory

• Reduce batch size
• Use smaller input resolution
• Use fewer models in the ensemble
• Enable mixed precision (FP16)

Fusion results worse than single model

• Check model weights are correct
• Tune iou_threshold for your data
• Ensure models are complementary (different architectures/sizes)
• Verify confidence thresholds