BiRefNet TensorRT Engine
Results and visual examples
Historical archive. Fury's maintained BiRefNet route is the pinned PyTorch lite-matting model. The prior TensorRT execution path was retired after repeated-call alpha-matte drift; a single Torch/TRT parity image would not establish repeat determinism. These engines are not the current maintained serving route.
Available build records: artifacts/birefnet.cutout@v1/h100-sm90/3c7d54ccd0644c963e9526b47a5b39eb9c7a045a1066ee0a41e2d19f08018b25/runtime-metadata.json, artifacts/birefnet.cutout@v1/rtx5090-sm120/1dd326d35db80e1c5db8cba126155fd8e3cd4416053316ff3434b3bb5dede5dc/runtime-metadata.json, artifacts/birefnet.cutout@v1/rtx5090-sm120/3c7d54ccd0644c963e9526b47a5b39eb9c7a045a1066ee0a41e2d19f08018b25/runtime-metadata.json, artifacts/birefnet.cutout@v1/rtx5090-sm120/60133ea26f81cb9ef3d48947685bf513896113171dc7bfdcd7127333f930605c/runtime-metadata.json, artifacts/birefnet.cutout@v1/rtx5090-sm120/9fc8590ceb69ac518e7c98eb36d5e28a4572b1a08e0af0e71d42f76c4c6fa788/runtime-metadata.json, metadata.json.
Source notebooks and reports
No checkpoint-matched photo gallery was located in the existing evidence reviewed for this update. The files and runtime records below are the available evidence; no visual quality claim is inferred from their presence.
Evidence provenance
This documentation update reuses saved results; it does not rerun inference or change weights. The starting repository revision is fd24be593b7c. Captions distinguish model predictions, training diagnostics, and aggregate metrics. Qualitative examples are not a representative accuracy estimate.
Gallery sources and SHA-256 checksums.
Pre-built TensorRT engine for BiRefNet background removal.
Specifications
| Setting | Value |
|---|---|
| Resolution | 1024x1024 |
| Batch Size | 1-4 |
| Precision | fp16 |
| TensorRT | 10.14.1.48 |
| Built on | NVIDIA GeForce RTX 4090 |
| Engine Size | 505.64 MB |
Usage
The engine is automatically downloaded when using BiRefNet with TensorRT enabled.
from ai.models.birefnet import BiRefNetSegmentationModel
model = BiRefNetSegmentationModel(use_tensorrt=True)
result = await model.run(image, payload=payload)
Rebuild
make run-ai-local -- python -m scripts.tensorrt.birefnet --force
Built at: 2026-01-22 07:46:24 UTC