YOLO11m-seg on NVIDIA T4 (server): 1 row.
Method v1.0 · dataset 2026-09-07 · verified 2026-09-07 · last updated September 2026
yolo11_segm-seg on NVIDIA T4 (server): 1 row (1 measured, 0 derived). Expected: FP16 158.7 fps.
Change hardware, precision, runtime or add a comparison set on the live engine.
Expected range per precision
Min / median / max of the matching rows, all classes. When a precision has no row, the estimator's benchmark hierarchy (B → C → D) supplies a fallback with its tier chain reported.
| Precision | Metric | N | Min | Median | Max | Best class | Measured |
|---|---|---|---|---|---|---|---|
| FP16 | fps | 1 | 158.7 | 158.7 | 158.7 | C | 1 |
Rows
| Precision | Runtime | Input | Power mode | Software | Metric | Class | Date | Source | Derivation | Dupes |
|---|---|---|---|---|---|---|---|---|---|---|
| FP16 | tensorrt (inferred) | 640x640 b1 | default | — | 158.7 fps | C | 2024-Q4 | Ultralytics · ultralytics:models/yolo11 · verified 2026-09-07 | 0 |
Cross-hardware comparison
Best row per hardware for the same model and precision, relative to the fastest.
| Hardware | Best value | Class |
|---|---|---|
| NVIDIA T4 (server) (reference) | 158.7 fps | C |
Assumptions
- NVIDIA T4 (server) is a reference platform in the benchmark database only; it is not a catalog module and no other engine sizes it.
- Rows from the research corpus do not state a runtime; the platform's native runtime (TensorRT / Edge TPU / Hailo SDK / RKNN / CPU) is assumed and flagged runtime_inferred.
- Class C = measured and published by the source named on the row; class D = EdgeAIStack-derived (derivation stated). Rows are inference-only unless scope says end_to_end; publisher measurement conditions (JetPack, batch, warm-up) are in the linked page.
Method and limitations
Rows are read from the normalised benchmark database (publisher benchmarks and research-derived rows, deduplicated per hardware × model × runtime × precision × input × power mode, best evidence kept). Expected range = min / median / max of the matching rows per precision; when a precision has no row the estimator's benchmark hierarchy (B → C → D) is consulted and its tier chain reported. Comparison = best row per hardware for the same model and precision, relative to the fastest. Nothing is scored; every number carries its class and source line.
Full method, dedupe rule, evidence classes and the confidence rule: Benchmark Explorer methodology.
Measure it yourself.
Nothing measured for your exact config, or want to confirm a class-D row? Run the measurement protocol on your board and submit the result — a human checks it against the protocol before it becomes a class-C row with your attribution.
Links
- Live engine (this exact query): https://edgeaistack.ai/engines/benchmark-explorer/?hw=nvidia_t4&model=yolo11_seg&variant=m-seg&mv=1.0&dv=2026-09-07
- Methodology: /methodology/benchmark-explorer/
- Measurement protocol: /methodology/measurement-protocol/
- Dataset: /datasets/benchmarks-v2.json
- NVIDIA T4 (server) overview: /benchmarks/nvidia-t4/
Change one input and re-run.
The live engine keeps every source line and gives you a fresh permalink and summary.