YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
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