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1

Publish the 2026Q3 edition of yolo11-seg

#1
by sebastientaylor - opened
base: refs/heads/main
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from: refs/pr/1
Discussion Files changed
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Files changed (39) hide show
  1. .gitattributes +3 -0
  2. README.md +286 -135
  3. ara240/yolo11m-seg-int16.dvm +2 -2
  4. ara240/yolo11n-seg-int16.dvm +2 -2
  5. ara240/yolo11s-seg-int16.dvm +2 -2
  6. hailo/yolo11m-seg-int8.hailo8l.hef +2 -2
  7. hailo/yolo11n-seg-int8.hailo8l.hef +2 -2
  8. hailo/yolo11s-seg-int8.hailo8l.hef +2 -2
  9. imx95/yolo11n-seg-int8-smart.imx95.tflite +2 -2
  10. imx95/yolo11n-seg-int8.imx95.tflite +2 -2
  11. imx95/yolo11s-seg-int8-smart.imx95.tflite +3 -0
  12. imx95/yolo11s-seg-int8.imx95.tflite +3 -0
  13. jetson/yolo11m-seg-fp16.engine +2 -2
  14. jetson/yolo11m-seg-int8.engine +3 -0
  15. jetson/yolo11n-seg-fp16.engine +2 -2
  16. jetson/yolo11n-seg-int8.engine +3 -0
  17. jetson/yolo11s-seg-fp16.engine +2 -2
  18. jetson/yolo11s-seg-int8.engine +3 -0
  19. onnx/yolo11m-seg-fp16.onnx +2 -2
  20. onnx/yolo11m-seg-fp32.onnx +2 -2
  21. onnx/yolo11n-seg-fp16.onnx +2 -2
  22. onnx/yolo11n-seg-fp32.onnx +2 -2
  23. onnx/yolo11s-seg-fp16.onnx +2 -2
  24. onnx/yolo11s-seg-fp32.onnx +2 -2
  25. qnn/yolo11m-seg-int16-smart.iq9075.qnn.onnx +3 -0
  26. qnn/yolo11m-seg-int8-logical.iq9075.qnn.onnx +3 -0
  27. qnn/yolo11m-seg-int8-smart.iq9075.qnn.onnx +3 -0
  28. qnn/yolo11n-seg-int16-smart.iq9075.qnn.onnx +3 -0
  29. qnn/yolo11n-seg-int8-logical.iq9075.qnn.onnx +3 -0
  30. qnn/yolo11n-seg-int8-smart.iq9075.qnn.onnx +3 -0
  31. qnn/yolo11s-seg-int16-smart.iq9075.qnn.onnx +3 -0
  32. qnn/yolo11s-seg-int8-logical.iq9075.qnn.onnx +3 -0
  33. qnn/yolo11s-seg-int8-smart.iq9075.qnn.onnx +3 -0
  34. tflite/yolo11m-seg-int8-smart.tflite +2 -2
  35. tflite/yolo11m-seg-int8.tflite +2 -2
  36. tflite/yolo11n-seg-int8-smart.tflite +2 -2
  37. tflite/yolo11n-seg-int8.tflite +2 -2
  38. tflite/yolo11s-seg-int8-smart.tflite +2 -2
  39. tflite/yolo11s-seg-int8.tflite +2 -2
.gitattributes CHANGED
@@ -42,3 +42,6 @@ hailo/yolo11s-seg-int8.hailo8l.hef filter=lfs diff=lfs merge=lfs -text
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  jetson/yolo11m-seg-fp16.engine filter=lfs diff=lfs merge=lfs -text
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  jetson/yolo11n-seg-fp16.engine filter=lfs diff=lfs merge=lfs -text
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  jetson/yolo11s-seg-fp16.engine filter=lfs diff=lfs merge=lfs -text
 
 
 
 
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  jetson/yolo11m-seg-fp16.engine filter=lfs diff=lfs merge=lfs -text
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  jetson/yolo11n-seg-fp16.engine filter=lfs diff=lfs merge=lfs -text
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  jetson/yolo11s-seg-fp16.engine filter=lfs diff=lfs merge=lfs -text
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+ jetson/yolo11m-seg-int8.engine filter=lfs diff=lfs merge=lfs -text
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+ jetson/yolo11n-seg-int8.engine filter=lfs diff=lfs merge=lfs -text
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+ jetson/yolo11s-seg-int8.engine filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -25,22 +25,22 @@ model-index:
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  metrics:
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  - name: "Box mAP@0.5 (Nano ONNX FP32)"
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  type: map_50
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- value: 53.27
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  - name: "Mask mAP@0.5-0.95 (Nano ONNX FP32)"
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  type: map
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- value: 30.75
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  - name: "Box mAP@0.5 (Small ONNX FP32)"
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  type: map_50
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- value: 61.72
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  - name: "Mask mAP@0.5-0.95 (Small ONNX FP32)"
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  type: map
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- value: 35.72
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  - name: "Box mAP@0.5 (Medium ONNX FP32)"
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  type: map_50
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- value: 67.11
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  - name: "Mask mAP@0.5-0.95 (Medium ONNX FP32)"
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  type: map
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- value: 39.43
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  ---
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  # YOLO11 Segmentation — EdgeFirst Model Zoo
@@ -56,7 +56,8 @@ YOLO11 Segmentation models trained on [COCO 2017](https://edgefirst.studio/publi
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  Part of the [EdgeFirst Model Zoo](https://huggingface.co/spaces/EdgeFirst/Models). New here? **[Introducing the EdgeFirst Model Zoo](https://huggingface.co/blog/EdgeFirst/model-zoo-intro)** covers what is in the zoo, why every figure links to its validation session, and how to reproduce any of it on your own hardware. Want a platform, model or task we don't cover yet? [Vote on what we measure next](https://github.com/orgs/EdgeFirstAI/discussions/categories/polls).
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  > [!TIP]
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- > **Training experiment**: [View on EdgeFirst Studio](https://edgefirst.studio/public/p-695/exp-886/trainings) — dataset, training configuration, metrics, and exported artifacts.
 
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  > [!NOTE]
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  > Architecture with C3k2 attention blocks.
@@ -69,9 +70,9 @@ Accuracy ceiling for each size, measured against COCO `val2017` (5,000 images) w
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  | Size | Params | GFLOPs | Box mAP@0.5 | Box mAP@0.5-0.95 | Mask mAP@0.5 | Mask mAP@0.5-0.95 | Source |
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  |------|--------|--------|-------------|------------------|--------------|-------------------|--------|
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- | Nano | 2.6M | 6.5 | 53.27% | 38.14% | 50.06% | 30.75% | [v-e97](https://edgefirst.studio/public/validation/v-e97/details?mode=charts) |
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- | Small | 9.4M | 21.5 | 61.72% | 45.35% | 58.20% | 35.72% | [v-e98](https://edgefirst.studio/public/validation/v-e98/details?mode=charts) |
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- | Medium | 20.1M | 68.0 | 67.11% | 50.47% | 63.55% | 39.43% | [v-e9a](https://edgefirst.studio/public/validation/v-e9a/details?mode=charts) |
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  | Large | 25.3M | 87.6 | — | — | — | — | — |
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  | XLarge | 56.9M | 195.0 | — | — | — | — | — |
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@@ -94,122 +95,158 @@ Row conventions in the table below:
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  - **Rows whose `Δ` cell reads `ref`** are the float reference runs each quantized/compiled measurement is graded against.
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  - **Rows without a number** under the metric columns are validation sessions currently in progress, or a session not yet linked to its ONNX FP32 reference. The Studio Source link tracks the current status.
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  - **Rows whose `Δ vs FP32` cell carries a ⚠** are below our accuracy expectations for that platform (more than 10 percentage points under the float reference). The numbers are real measurements on real hardware, reproducible from the linked Studio session, and we publish them as-is; we are investigating the results to make improvements, and the next snapshot of this card will reflect any recovered accuracy.
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- - **Rows whose metric cells read `In progress`** indicate platforms where this model family already runs on target but accuracy work is still in progress with the silicon vendor, so we withhold the numbers until that work lands. The Studio source link tracks the session; the next snapshot of this card will publish the measured results once resolved.
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  - **Precision** varies by target: the ONNX reference rows are FP32; macOS CoreML and NVIDIA Jetson TensorRT run FP16; the NXP i.MX 8M Plus, NXP i.MX 95 Neutron, and Hailo NPUs run INT8. The NXP Ara240 DNPU runs a **mixed INT8/INT16** scheme — most of the model is INT8, with the box-regression path (and the ops feeding it) promoted to INT16 to improve localization accuracy.
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  - **Decoder variants.** EdgeFirst ships three INT8 split-decoders — **smart**, **logical**, and **combined** (described under *Decode* below). Every one we have measured is listed; none is headlined over another, because which one wins is a property of the target, not of the variant. Full converter documentation: [EdgeFirst model conversion](https://doc.edgefirst.ai/latest/models/conversion/) — these are the converters used by this Model Zoo and the EdgeFirst Performance Index report.
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  - **Runtime.** Which inference stack executed the model. It is a column of its own because one accelerator can be reached by more than one: on Qualcomm Hexagon HTP, `ONNX · QNN EP` is an ONNX Runtime EPContext binary while `LiteRT` is the LiteRT + QNN delegate path, and the two produce measurably different pipelines from the same weights on the same silicon. Compare rows that share a size, platform, precision, and decode to read that difference directly.
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  - **Decode.** What the exported graph emits, and therefore what your application has to do with it. **Logical** keeps the head's outputs separate and named — boxes, scores, and for segmentation the mask prototypes and coefficients — so reading them needs no decoder beyond taking the tensors as they come. **Smart** cuts the graph further upstream, at a point chosen by dynamic range and sliceability: the operations that quantize worst are lifted out of the model and run outside it, which is what recovers the accuracy, and which is why it needs a decoder implementing that cut (in EdgeFirst deployments, the HAL provides it). **Combined** fuses the decode into the quantized graph — the standard upstream export. It is the least accurate of the three by construction, and the EdgeFirst converters do not emit it for quantized targets; rows appear only where one was measured. Compare rows sharing a size and platform to see what the cut buys on your target — on detection it is often small, on quantized segmentation masks it is usually decisive. A `—` means the run carries no quantized decode split at all (float ONNX / TensorRT / CoreML).
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  - **Rows are not ranked.** Where a size and platform appear more than once, the rows are competing configurations — a different runtime, precision, decode variant, pipeline mode, or board — not duplicates. Only genuinely repeated measurements of the *same* configuration are collapsed, newest first.
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- - **Platform-label suffixes.** `(FRDM)` / `(Phytec)` name the NXP i.MX 95 development board a session ran on. `— latency` / `— throughput` mark the two pipeline configurations the NXP i.MX 95 Neutron and NXP Ara240 targets run: the **latency** pipeline runs inference serially for the lowest per-frame latency; the **throughput** pipeline runs multiple inference workers for the highest FPS, which raises per-call inference time in exchange. Rows with neither suffix run a single pipeline.
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- - **End-to-end (ms)** is the sequential per-image latency of the compute pipeline — preprocess → inference → postprocess. Image acquisition (camera or file load + JPEG decode) overlaps these stages and is excluded from this figure.
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- - **Realized FPS vs Core-throughput ceiling (FPS).** **Realized FPS** is the *measured* steady-state throughput — the rate at which final results are actually delivered over the full validation pipeline. It normally exceeds `1000 / end-to-end` because the runtime overlaps stages across frames, and it is the **true, priority number**. **Core-throughput ceiling (FPS)** (shown with a `~`) is the accelerator's **core** ceiling — `1000 / device-compute-time`, the rate the NPU/DNPU could sustain *if it were the only bottleneck* — so it is a **possibly-achievable note, not a claim**. It is read from the isolated device-compute stage, which (unlike the host capture/preprocess stages, whose measured time inflates when the pipeline is backpressured) is stable and load-independent. Whether a deployment approaches it depends on the surrounding pipeline, and two levers dominate: (1) **host bottlenecks** — these validation runs decode a JPEG per image, whereas a live **camera pipeline** skips that decode and can run closer to the ceiling; and (2) **confidence threshold** — validation runs at `0.001` to capture every detection for mAP, which makes NMS/decode heavy, while a deployment threshold of `0.25–0.75` produces far fewer candidate boxes and lighter postprocessing, raising realized FPS toward the ceiling.
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-
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- | Size | Platform | Runtime | Decode | Box mAP@0.5 | Mask mAP@0.5-0.95 | Δ mask vs FP32 (pp) | Inference (ms) | End-to-end (ms) | Realized FPS | Core-throughput ceiling (FPS) | Source |
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- |------|----------|---------|--------|-------------|-------------------|---------------------|----------------|-----------------|--------------|-------------------------------|--------|
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- | Nano | ONNX FP32 (AWS Graviton · 4-core) | ONNX | — | 53.30% | 30.74% | -0.01 | 338.67 | 374.17 | 11.4 | ~3 | [v-ed7](https://edgefirst.studio/public/validation/v-ed7/details?mode=charts) |
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- | Nano | ONNX FP32 (AWS Graviton4 · 48-core) | ONNX | — | 53.30% | 30.74% | -0.01 | 98.47 | 124.43 | 121.1 | ~10 | [v-eed](https://edgefirst.studio/public/validation/v-eed/details?mode=charts) |
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- | Nano | ONNX FP32 (AWS Graviton4 · 8-core) | ONNX | — | 53.30% | 30.74% | -0.01 | 284.66 | 306.90 | 26.9 | ~4 | [v-ee4](https://edgefirst.studio/public/validation/v-ee4/details?mode=charts) |
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- | Nano | ONNX FP32 (Intel Core i9-13900F · 32-core) | ONNX | — | 53.28% | 30.74% | -0.01 | 51.98 | 77.65 | 51.2 | ~51 | [v-a4a](https://edgefirst.studio/public/validation/v-a4a/details?mode=charts) |
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- | Nano | ONNX FP32 (Intel Xeon Platinum 8488C · 24-core) | ONNX | — | 53.28% | 30.74% | -0.01 | 94.20 | 134.37 | 105.0 | ~11 | [v-eaf](https://edgefirst.studio/public/validation/v-eaf/details?mode=charts) |
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- | Nano | ONNX FP32 (Intel Xeon Platinum 8488C · 4-core) | ONNX | — | 53.28% | 30.74% | -0.01 | 216.36 | 260.38 | 33.2 | ~5 | [v-ea6](https://edgefirst.studio/public/validation/v-ea6/details?mode=charts) |
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- | Nano | ONNX FP32 (CUDA) | ONNX | — | 53.27% | 30.75% | ref | 12.39 | 40.66 | 129.6 | ~146 | [v-e97](https://edgefirst.studio/public/validation/v-e97/details?mode=charts) |
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- | Nano | ONNX FP32 (CUDA) | ONNX | — | 53.28% | 30.75% | +0.00 | 10.76 | 21.15 | 244.6 | ~244 | [v-a8c](https://edgefirst.studio/public/validation/v-a8c/details?mode=charts) |
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- | Nano | ONNX FP16 (CUDA) | ONNX | — | 53.29% | 30.74% | -0.01 | 8.42 | 19.60 | 305.4 | ~305 | [v-aa1](https://edgefirst.studio/public/validation/v-aa1/details?mode=charts) |
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- | Nano | Apple M2 Max — CoreML Neural Engine (FP16) | ONNX | — | 52.89% | 30.50% | -0.25 | 2.74 | 12.68 | 349.9 | ~351 | [v-9f0](https://edgefirst.studio/public/validation/v-9f0/details?mode=charts) |
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- | Nano | Apple M2 Max — CoreML Metal GPU (FP16) | ONNX | — | 51.85% | 29.94% | -0.81 | 6.86 | 18.07 | 290.7 | ~291 | [v-739](https://edgefirst.studio/public/validation/v-739/details?mode=charts) |
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- | Nano | Apple M2 Max — CoreML CPU (FP16) | ONNX | — | 52.85% | 30.51% | -0.24 | 20.86 | 29.03 | 90.6 | ~91 | [v-9f1](https://edgefirst.studio/public/validation/v-9f1/details?mode=charts) |
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- | Nano | Apple iPhone 17 Pro — CoreML Neural Engine (FP16) | ONNX | — | 52.89% | 30.49% | -0.26 | 3.70 | 28.05 | 143.3 | ~56 | [v-f67](https://edgefirst.studio/public/validation/v-f67/details?mode=charts) |
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- | Nano | Apple iPhone 17 Pro — CoreML Metal GPU (FP16) | ONNX | — | 52.91% | 30.52% | -0.23 | 13.11 | 41.04 | 100.7 | ~51 | [v-f66](https://edgefirst.studio/public/validation/v-f66/details?mode=charts) |
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- | Nano | Apple iPhone 17 Pro — CoreML CPU (FP16) | ONNX | — | 52.85% | 30.48% | -0.27 | 27.87 | 50.78 | 65.1 | ~36 | [v-f64](https://edgefirst.studio/public/validation/v-f64/details?mode=charts) |
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- | Nano | Apple iPhone 15 Pro — CoreML Neural Engine (FP16) | ONNX | — | 52.90% | 30.48% | -0.27 | 3.80 | 36.07 | 107.7 | ~42 | [v-f25](https://edgefirst.studio/public/validation/v-f25/details?mode=charts) |
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- | Nano | Apple iPhone 15 Pro — CoreML Metal GPU (FP16) | ONNX | — | 52.91% | 30.53% | -0.22 | 21.37 | 66.59 | 63.1 | ~30 | [v-f26](https://edgefirst.studio/public/validation/v-f26/details?mode=charts) |
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- | Nano | Apple iPhone 15 Pro — CoreML CPU (FP16) | ONNX | — | 52.85% | 30.50% | -0.25 | 37.61 | 68.23 | 48.3 | ~27 | [v-f00](https://edgefirst.studio/public/validation/v-f00/details?mode=charts) |
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- | Nano | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT8) | ONNX · QNN EP | Smart | 48.69% | 27.48% | -3.27 | 2.37 | 40.94 | 97.8 | ~169 | [v-10a9](https://edgefirst.studio/public/validation/v-10a9/details?mode=charts) |
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- | Nano | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT8) | ONNX · QNN EP | Logical | 37.27% | 18.48% | -12.27 | 2.37 | 14.06 | 342.4 | ~423 | [v-10ac](https://edgefirst.studio/public/validation/v-10ac/details?mode=charts) |
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- | Nano | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT16) | ONNX · QNN EP | Smart | 51.20% | 29.21% | -1.54 | 5.76 | 48.68 | 87.8 | ~135 | [v-10a6](https://edgefirst.studio/public/validation/v-10a6/details?mode=charts) |
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- | Nano | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT16) | ONNX · QNN EP | Logical | 50.14% | 11.13% | -19.62 | 6.03 | 27.84 | 151.3 | ~209 | [v-10a7](https://edgefirst.studio/public/validation/v-10a7/details?mode=charts) |
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- | Nano | Samsung Galaxy S26 Ultra — CPU | LiteRT | Smart | 50.86% | 28.96% | -1.79 | 84.17 | 147.95 | 53.1 | ~12 | [v-fc5](https://edgefirst.studio/public/validation/v-fc5/details?mode=charts) |
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- | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Combined | 37.52% | 21.48% | -9.27 | 108.51 | 153.78 | 8.3 | ~9 | [v-8c9](https://edgefirst.studio/public/validation/v-8c9/details?mode=charts) |
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- | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Logical | 37.52% | 21.48% | -9.27 | 108.50 | 149.78 | 8.3 | ~9 | [v-8cd](https://edgefirst.studio/public/validation/v-8cd/details?mode=charts) |
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- | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Smart | 48.41% | 27.17% | -3.58 | 104.78 | 200.77 | 8.6 | ~9 | [v-8d1](https://edgefirst.studio/public/validation/v-8d1/details?mode=charts) |
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- | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) — latency | LiteRT | Logical | 37.52% | 21.48% | -9.27 | 110.06 | 162.41 | 8.2 | ~9 | [v-c86](https://edgefirst.studio/public/validation/v-c86/details?mode=charts) |
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- | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) — throughput | LiteRT | Smart | 48.41% | 27.17% | -3.58 | 105.33 | 214.20 | 8.5 | ~9 | [v-c85](https://edgefirst.studio/public/validation/v-c85/details?mode=charts) |
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- | Nano | NXP i.MX 95 + eIQ Neutron NPU (FRDM) — latency | LiteRT | Logical | In progress | — | — | — | — | — | — | [v-8b9](https://edgefirst.studio/public/validation/v-8b9/details?mode=charts) |
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- | Nano | NXP i.MX 95 + eIQ Neutron NPU (FRDM) — latency | LiteRT | Combined | In progress | — | — | — | — | — | — | [v-8b6](https://edgefirst.studio/public/validation/v-8b6/details?mode=charts) |
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- | Nano | NXP i.MX 95 + eIQ Neutron NPU (FRDM) — throughput | LiteRT | Smart | In progress | — | — | — | — | — | — | [v-8be](https://edgefirst.studio/public/validation/v-8be/details?mode=charts) |
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- | Nano | NXP i.MX 95 + eIQ Neutron NPU (FRDM) — throughput | LiteRT | Logical | In progress | — | — | — | — | — | — | [v-8bb](https://edgefirst.studio/public/validation/v-8bb/details?mode=charts) |
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- | Nano | NXP i.MX 95 + eIQ Neutron NPU (FRDM) — throughput | LiteRT | Combined | In progress | — | — | — | — | — | — | [v-8b8](https://edgefirst.studio/public/validation/v-8b8/details?mode=charts) |
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- | Nano | NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput | LiteRT | Smart | In progress | — | — | — | — | — | — | [v-89c](https://edgefirst.studio/public/validation/v-89c/details?mode=charts) |
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- | Nano | NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency | LiteRT | Logical | In progress | — | — | — | — | — | — | [v-bd1](https://edgefirst.studio/public/validation/v-bd1/details?mode=charts) |
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- | Nano | NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput | LiteRT | Smart | In progress | — | — | — | — | — | — | [v-e0e](https://edgefirst.studio/public/validation/v-e0e/details?mode=charts) |
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- | Nano | NXP Ara240 (FRDM) — latency | Ara DVM | Smart | 49.18% | 28.03% | -2.72 | 9.56 | 40.86 | 57.4 | ~63 | [v-a1a](https://edgefirst.studio/public/validation/v-a1a/details?mode=charts) |
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- | Nano | NXP Ara240 (FRDM) — throughput | Ara DVM | Smart | 49.18% | 28.03% | -2.72 | 9.80 | 61.08 | 58.1 | ~229 | [v-a1b](https://edgefirst.studio/public/validation/v-a1b/details?mode=charts) |
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- | Nano | Raspberry Pi 5 + Hailo-8L NPU | Hailo HEF | — | 51.15% | 29.32% | -1.43 | 24.26 | 43.75 | 40.4 | ~40 | [v-a44](https://edgefirst.studio/public/validation/v-a44/details?mode=charts) |
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- | Nano | NVIDIA Jetson Orin Nano (TensorRT FP16) | TensorRT | — | 53.27% | 30.77% | +0.02 | 6.84 | 58.61 | 79.2 | ~66 | [v-91b](https://edgefirst.studio/public/validation/v-91b/details?mode=charts) |
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- | Small | ONNX FP32 (AWS Graviton · 4-core) | ONNX | — | 61.74% | 35.71% | -0.01 | 953.20 | 979.26 | 4.2 | ~1 | [v-ed8](https://edgefirst.studio/public/validation/v-ed8/details?mode=charts) |
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- | Small | ONNX FP32 (AWS Graviton4 · 8-core) | ONNX | — | 61.74% | 35.71% | -0.01 | 789.81 | 803.95 | 10.0 | ~1 | [v-ee8](https://edgefirst.studio/public/validation/v-ee8/details?mode=charts) |
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- | Small | ONNX FP32 (AWS Graviton4 · 48-core) | ONNX | — | 61.74% | 35.71% | -0.01 | 261.88 | 278.31 | 48.7 | ~4 | [v-ee1](https://edgefirst.studio/public/validation/v-ee1/details?mode=charts) |
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- | Small | ONNX FP32 (Intel Core i9-13900F · 32-core) | ONNX | — | 61.72% | 35.73% | +0.01 | 128.91 | 151.38 | 21.5 | ~21 | [v-a51](https://edgefirst.studio/public/validation/v-a51/details?mode=charts) |
153
- | Small | ONNX FP32 (Intel Xeon Platinum 8488C · 24-core) | ONNX | — | 61.72% | 35.72% | +0.00 | 232.11 | 268.33 | 53.1 | ~4 | [v-eb2](https://edgefirst.studio/public/validation/v-eb2/details?mode=charts) |
154
- | Small | ONNX FP32 (Intel Xeon Platinum 8488C · 4-core) | ONNX | — | 61.72% | 35.72% | +0.00 | 458.07 | 484.61 | 17.0 | ~2 | [v-eae](https://edgefirst.studio/public/validation/v-eae/details?mode=charts) |
155
- | Small | ONNX FP32 (CUDA) | ONNX | — | 61.72% | 35.72% | ref | 14.47 | 39.23 | 143.8 | ~151 | [v-e98](https://edgefirst.studio/public/validation/v-e98/details?mode=charts) |
156
- | Small | ONNX FP32 (CUDA) | ONNX | — | 61.72% | 35.72% | +0.00 | 19.67 | 30.87 | 153.4 | ~153 | [v-a93](https://edgefirst.studio/public/validation/v-a93/details?mode=charts) |
157
- | Small | ONNX FP16 (CUDA) | ONNX | — | 61.69% | 35.73% | +0.01 | 13.04 | 25.38 | 224.4 | ~224 | [v-aa8](https://edgefirst.studio/public/validation/v-aa8/details?mode=charts) |
158
- | Small | Apple M2 Max — CoreML Neural Engine (FP16) | ONNX | — | 60.16% | 34.89% | -0.83 | 6.81 | 13.26 | 253.7 | ~254 | [v-730](https://edgefirst.studio/public/validation/v-730/details?mode=charts) |
159
- | Small | Apple M2 Max — CoreML Metal GPU (FP16) | ONNX | — | 61.03% | 35.29% | -0.43 | 21.32 | 29.98 | 134.1 | ~134 | [v-9f2](https://edgefirst.studio/public/validation/v-9f2/details?mode=charts) |
160
- | Small | Apple M2 Max — CoreML CPU (FP16) | ONNX | — | 61.03% | 35.29% | -0.43 | 40.34 | 47.99 | 48.1 | ~48 | [v-9f3](https://edgefirst.studio/public/validation/v-9f3/details?mode=charts) |
161
- | Small | Apple iPhone 17 Pro — CoreML Neural Engine (FP16) | ONNX | — | 60.98% | 35.34% | -0.38 | 11.72 | 26.34 | 141.3 | ~85 | [v-f6f](https://edgefirst.studio/public/validation/v-f6f/details?mode=charts) |
162
- | Small | Apple iPhone 17 Pro — CoreML Metal GPU (FP16) | ONNX | — | 61.06% | 35.32% | -0.40 | 27.81 | 46.06 | 65.9 | ~36 | [v-f70](https://edgefirst.studio/public/validation/v-f70/details?mode=charts) |
163
- | Small | Apple iPhone 17 Pro — CoreML CPU (FP16) | ONNX | — | 61.03% | 35.30% | -0.42 | 67.52 | 84.97 | 28.9 | ~15 | [v-f6e](https://edgefirst.studio/public/validation/v-f6e/details?mode=charts) |
164
- | Small | Apple iPhone 15 Pro — CoreML Neural Engine (FP16) | ONNX | — | 60.98% | 35.35% | -0.37 | 11.73 | 41.90 | 109.1 | ~48 | [v-f3a](https://edgefirst.studio/public/validation/v-f3a/details?mode=charts) |
165
- | Small | Apple iPhone 15 Pro — CoreML Metal GPU (FP16) | ONNX | — | 61.07% | 35.33% | -0.39 | 68.30 | 81.83 | 28.5 | ~15 | [v-f3e](https://edgefirst.studio/public/validation/v-f3e/details?mode=charts) |
166
- | Small | Apple iPhone 15 Pro — CoreML CPU (FP16) | ONNX | — | 61.02% | 35.30% | -0.42 | 75.69 | 97.85 | 25.7 | ~13 | [v-f03](https://edgefirst.studio/public/validation/v-f03/details?mode=charts) |
167
- | Small | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT8) | ONNX · QNN EP | Smart | 59.73% | 34.12% | -1.60 | 3.21 | 34.36 | 120.1 | ~195 | [v-10b2](https://edgefirst.studio/public/validation/v-10b2/details?mode=charts) |
168
- | Small | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT8) | ONNX · QNN EP | Logical | 46.27% | 22.39% | -13.33 | 3.19 | 15.20 | 331.4 | ~409 | [v-10b4](https://edgefirst.studio/public/validation/v-10b4/details?mode=charts) |
169
- | Small | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT16) | ONNX · QNN EP | Smart | 61.09% | 35.14% | -0.58 | 5.99 | 49.31 | 87.0 | ~133 | [v-10ad](https://edgefirst.studio/public/validation/v-10ad/details?mode=charts) |
170
- | Small | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT16) | ONNX · QNN EP | Logical | 60.16% | 12.52% | -23.20 | 7.26 | 32.86 | 141.3 | ~185 | [v-10b0](https://edgefirst.studio/public/validation/v-10b0/details?mode=charts) |
171
- | Small | Samsung Galaxy S26 Ultra — CPU | LiteRT | Smart | 60.56% | 34.69% | -1.03 | 166.45 | 216.39 | 32.8 | ~6 | [v-100c](https://edgefirst.studio/public/validation/v-100c/details?mode=charts) |
172
- | Small | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Combined | 32.07% | 16.65% | -19.07 | 201.05 | 243.45 | 4.7 | ~5 | [v-942](https://edgefirst.studio/public/validation/v-942/details?mode=charts) |
173
- | Small | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Logical | 32.07% | 16.65% | -19.07 | 201.08 | 241.56 | 4.7 | ~5 | [v-94d](https://edgefirst.studio/public/validation/v-94d/details?mode=charts) |
174
- | Small | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Smart | 42.80% | 22.19% | -13.53 ⚠ | 198.08 | 285.44 | 4.8 | ~5 | [v-955](https://edgefirst.studio/public/validation/v-955/details?mode=charts) |
175
- | Small | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) — latency | LiteRT | Logical | 32.07% | 16.65% | -19.07 | 203.33 | 254.84 | 4.6 | ~5 | [v-c98](https://edgefirst.studio/public/validation/v-c98/details?mode=charts) |
176
- | Small | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) — throughput | LiteRT | Smart | 42.80% | 22.19% | -13.53 ⚠ | 198.54 | 298.59 | 4.8 | ~5 | [v-c96](https://edgefirst.studio/public/validation/v-c96/details?mode=charts) |
177
- | Small | NXP Ara240 (FRDM) — latency | Ara DVM | Smart | 57.97% | 33.61% | -2.11 | 15.63 | 42.29 | 53.6 | ~54 | [v-a28](https://edgefirst.studio/public/validation/v-a28/details?mode=charts) |
178
- | Small | NXP Ara240 (FRDM) — throughput | Ara DVM | Smart | 57.97% | 33.61% | -2.11 | 16.15 | 66.74 | 63.7 | ~94 | [v-a29](https://edgefirst.studio/public/validation/v-a29/details?mode=charts) |
179
- | Small | Raspberry Pi 5 + Hailo-8L NPU | Hailo HEF | — | 59.50% | 34.43% | -1.29 | 38.47 | 57.35 | 23.8 | ~24 | [v-8ed](https://edgefirst.studio/public/validation/v-8ed/details?mode=charts) |
180
- | Small | NVIDIA Jetson Orin Nano (TensorRT FP16) | TensorRT | — | 61.68% | 35.73% | +0.01 | 15.14 | 64.68 | 88.1 | ~68 | [v-923](https://edgefirst.studio/public/validation/v-923/details?mode=charts) |
181
- | Medium | ONNX FP32 (AWS Graviton · 4-core) | ONNX | — | 67.13% | 39.45% | +0.02 | 3010.99 | 3034.53 | 1.3 | ~0 | [v-ed9](https://edgefirst.studio/public/validation/v-ed9/details?mode=charts) |
182
- | Medium | ONNX FP32 (AWS Graviton4 · 8-core) | ONNX | — | 67.13% | 39.45% | +0.02 | 2447.34 | 2458.34 | 3.3 | ~0 | [v-eea](https://edgefirst.studio/public/validation/v-eea/details?mode=charts) |
183
- | Medium | ONNX FP32 (AWS Graviton4 · 48-core) | ONNX | — | 67.13% | 39.45% | +0.02 | 833.72 | 852.30 | 15.3 | ~1 | [v-ee3](https://edgefirst.studio/public/validation/v-ee3/details?mode=charts) |
184
- | Medium | ONNX FP32 (Intel Core i9-13900F · 32-core) | ONNX | — | 67.10% | 39.43% | +0.00 | 364.67 | 385.66 | 7.9 | ~8 | [v-a58](https://edgefirst.studio/public/validation/v-a58/details?mode=charts) |
185
- | Medium | ONNX FP32 (Intel Xeon Platinum 8488C · 24-core) | ONNX | — | 67.10% | 39.43% | +0.00 | 675.69 | 710.38 | 18.6 | ~1 | [v-eb5](https://edgefirst.studio/public/validation/v-eb5/details?mode=charts) |
186
- | Medium | ONNX FP32 (Intel Xeon Platinum 8488C · 4-core) | ONNX | — | 67.10% | 39.43% | +0.00 | 1727.02 | 1753.18 | 4.6 | ~1 | [v-eb0](https://edgefirst.studio/public/validation/v-eb0/details?mode=charts) |
187
- | Medium | ONNX FP32 (CUDA) | ONNX | — | 67.11% | 39.43% | ref | 33.42 | 50.53 | 105.1 | ~105 | [v-e9a](https://edgefirst.studio/public/validation/v-e9a/details?mode=charts) |
188
- | Medium | ONNX FP32 (CUDA) | ONNX | — | 67.11% | 39.42% | -0.01 | 53.53 | 63.75 | 64.2 | ~64 | [v-a9a](https://edgefirst.studio/public/validation/v-a9a/details?mode=charts) |
189
- | Medium | ONNX FP16 (CUDA) | ONNX | — | 67.10% | 39.44% | +0.01 | 28.97 | 42.38 | 114.2 | ~114 | [v-aaf](https://edgefirst.studio/public/validation/v-aaf/details?mode=charts) |
190
- | Medium | Apple M2 Max — CoreML Neural Engine (FP16) | ONNX | — | 65.03% | 38.29% | -1.14 | 24.35 | 31.55 | 79.3 | ~79 | [v-7ff](https://edgefirst.studio/public/validation/v-7ff/details?mode=charts) |
191
- | Medium | Apple M2 Max — CoreML Metal GPU (FP16) | ONNX | — | 65.99% | 38.70% | -0.73 | 62.30 | 70.45 | 47.2 | ~47 | [v-9e1](https://edgefirst.studio/public/validation/v-9e1/details?mode=charts) |
192
- | Medium | Apple M2 Max — CoreML CPU (FP16) | ONNX | — | 65.96% | 38.67% | -0.76 | 90.94 | 98.48 | 21.7 | ~22 | [v-9e0](https://edgefirst.studio/public/validation/v-9e0/details?mode=charts) |
193
- | Medium | Apple iPhone 17 Pro — CoreML Neural Engine (FP16) | ONNX | — | 65.98% | 38.80% | -0.63 | 33.57 | 42.62 | 58.0 | ~30 | [v-f77](https://edgefirst.studio/public/validation/v-f77/details?mode=charts) |
194
- | Medium | Apple iPhone 17 Pro — CoreML Metal GPU (FP16) | ONNX | — | 66.00% | 38.72% | -0.71 | 85.00 | 95.86 | 22.6 | ~12 | [v-f78](https://edgefirst.studio/public/validation/v-f78/details?mode=charts) |
195
- | Medium | Apple iPhone 17 Pro — CoreML CPU (FP16) | ONNX | — | 65.98% | 38.66% | -0.77 | 177.14 | 191.83 | 11.2 | ~6 | [v-f7a](https://edgefirst.studio/public/validation/v-f7a/details?mode=charts) |
196
- | Medium | Apple iPhone 15 Pro — CoreML Neural Engine (FP16) | ONNX | — | 65.98% | 38.80% | -0.63 | 39.88 | 53.11 | 48.5 | ~25 | [v-f4c](https://edgefirst.studio/public/validation/v-f4c/details?mode=charts) |
197
- | Medium | Apple iPhone 15 Pro — CoreML Metal GPU (FP16) | ONNX | — | 66.00% | 38.71% | -0.72 | 212.26 | 228.95 | 9.2 | ~5 | [v-f61](https://edgefirst.studio/public/validation/v-f61/details?mode=charts) |
198
- | Medium | Apple iPhone 15 Pro — CoreML CPU (FP16) | ONNX | — | 65.98% | 38.68% | -0.75 | 211.81 | 234.39 | 9.3 | ~5 | [v-f06](https://edgefirst.studio/public/validation/v-f06/details?mode=charts) |
199
- | Medium | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT8) | ONNX · QNN EP | Smart | 65.06% | 38.08% | -1.35 | 5.68 | 38.77 | 115.2 | ~188 | [v-10ba](https://edgefirst.studio/public/validation/v-10ba/details?mode=charts) |
200
- | Medium | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT8) | ONNX · QNN EP | Logical | 49.44% | 28.14% | -11.29 | 7.17 | 20.32 | 228.2 | ~256 | [v-10bc](https://edgefirst.studio/public/validation/v-10bc/details?mode=charts) |
201
- | Medium | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT16) | ONNX · QNN EP | Smart | 66.15% | 38.72% | -0.71 | 13.19 | 57.10 | 76.1 | ~101 | [v-10b6](https://edgefirst.studio/public/validation/v-10b6/details?mode=charts) |
202
- | Medium | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT16) | ONNX · QNN EP | Logical | 65.18% | 13.50% | -25.93 | 22.17 | 44.17 | 83.2 | ~89 | [v-10b8](https://edgefirst.studio/public/validation/v-10b8/details?mode=charts) |
203
- | Medium | Samsung Galaxy S26 Ultra — CPU | LiteRT | Smart | 65.85% | 38.64% | -0.79 | 466.22 | 507.70 | 12.5 | ~2 | [v-1021](https://edgefirst.studio/public/validation/v-1021/details?mode=charts) |
204
- | Medium | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Combined | 40.48% | 22.86% | -16.57 | 396.73 | 437.38 | 2.5 | ~3 | [v-9a9](https://edgefirst.studio/public/validation/v-9a9/details?mode=charts) |
205
- | Medium | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Logical | 40.48% | 22.86% | -16.57 | 396.62 | 436.70 | 2.5 | ~3 | [v-9aa](https://edgefirst.studio/public/validation/v-9aa/details?mode=charts) |
206
- | Medium | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Smart | 54.71% | 30.31% | -9.12 ⚠ | 392.18 | 468.93 | 2.5 | ~3 | [v-9ab](https://edgefirst.studio/public/validation/v-9ab/details?mode=charts) |
207
- | Medium | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) — latency | LiteRT | Logical | 40.48% | 22.86% | -16.57 | 401.31 | 452.24 | 2.4 | ~2 | [v-ca9](https://edgefirst.studio/public/validation/v-ca9/details?mode=charts) |
208
- | Medium | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) — throughput | LiteRT | Smart | 54.71% | 30.31% | -9.12 ⚠ | 396.28 | 488.91 | 2.5 | ~3 | [v-ca8](https://edgefirst.studio/public/validation/v-ca8/details?mode=charts) |
209
- | Medium | NXP Ara240 (FRDM) — latency | Ara DVM | Smart | 62.87% | 37.36% | -2.07 | 39.06 | 61.74 | 24.8 | ~25 | [v-a36](https://edgefirst.studio/public/validation/v-a36/details?mode=charts) |
210
- | Medium | NXP Ara240 (FRDM) — throughput | Ara DVM | Smart | 62.85% | 37.35% | -2.08 | 39.18 | 63.09 | 29.1 | ~29 | [v-a37](https://edgefirst.studio/public/validation/v-a37/details?mode=charts) |
211
- | Medium | Raspberry Pi 5 + Hailo-8L NPU | Hailo HEF | — | 64.44% | 37.62% | -1.81 | 79.87 | 99.64 | 11.8 | ~12 | [v-934](https://edgefirst.studio/public/validation/v-934/details?mode=charts) |
212
- | Medium | NVIDIA Jetson Orin Nano (TensorRT FP16) | TensorRT | — | 67.07% | 39.43% | +0.00 | 72.33 | 97.67 | 54.8 | ~55 | [v-92c](https://edgefirst.studio/public/validation/v-92c/details?mode=charts) |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
213
 
214
  > **⚠ Below expectations — under investigation.** The rows marked ⚠ above measure more than 10 percentage points below the same training session's float reference: the model accuracy on that platform is below our expectations. We publish the measured numbers rather than hiding them, and we are investigating the results to make improvements — the next snapshot of this card will reflect any recovered accuracy.
215
 
@@ -224,9 +261,9 @@ These results are produced by the EdgeFirst on-target validation pipeline:
224
  2. **EdgeFirst Studio** receives the metrics, predictions, charts and trace, and publishes them as the `v-XXXX` validation session every row above cites. Studio is where results are published, compared and browsed — it is not where they are computed.
225
  3. **EdgeFirst HAL** ([open source](https://github.com/EdgeFirstAI/hal)) provides the hardware-accelerated preprocessing and post-decoding primitives used at both validation and deployment time, so the timings measured here reflect the same accelerated paths a production runtime would take.
226
 
227
- Inference latency is reported as the on-accelerator inference time. End-to-end latency is the sequential per-image latency across the compute pipeline — preprocessing, inference, and postprocessing; image acquisition (file or camera load and JPEG decode) overlaps these stages and is excluded from this figure.
228
 
229
- Two throughput figures are reported. **Realized FPS** is the *measured* steady-state rate at which final results are emitted, measured directly from the profiler's per-frame result-emission timestamps over the steady-state stream — trace-independent; the Perfetto trace's own FPS is used only as a fallback on sessions where that scalar isn't available. It is the true, priority number and generally exceeds `1000 / end-to-end` because the runtime overlaps stages across frames. **Core-throughput ceiling (FPS)** is the accelerator's **core** ceiling — `1000 / device-compute-time`, i.e. the throughput if the accelerator were the *only* bottleneck. It is taken from the isolated device-compute stage (on transfer-split runtimes the trace separates host↔device transfers from device compute), which is load-independent — unlike the host capture/preprocess service times, whose measured cost inflates under pipeline backpressure (the same 5000 JPEGs cost ~7.8 ms/frame serialized but far more under throughput backpressure), so the slowest-stage figure would *understate* a fast accelerator. It is a possibly-achievable ceiling, not a measured result: reaching it depends on the deployment pipeline. A validation run decodes a JPEG per image and evaluates at a `0.001` confidence threshold (to capture every detection for mAP), both of which load the host and postprocess stages; a production camera pipeline (no JPEG decode) at a deployment threshold of `0.25–0.75` (far fewer candidate boxes through NMS) moves realized throughput toward the core-throughput ceiling.
230
 
231
  See [EdgeFirst Studio](https://edgefirst.studio) for the full validation pipeline.
232
 
@@ -236,19 +273,133 @@ See [EdgeFirst Studio](https://edgefirst.studio) for the full validation pipelin
236
 
237
  Artifacts are organized by deployment target. Each model file embeds the EdgeFirst `edgefirst.json` metadata (training session, dataset version, calibration artifact, converter chain) so a single file is sufficient for deployment — no sidecar configuration required.
238
 
239
- Browse and download every artifact from the **[repository file tree](https://huggingface.co/EdgeFirst/yolo11-seg/tree/main)**. Files are organized into per-target folders and follow the naming convention `yolo11{size}-seg-{precision}[-smart]{extension}`:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
240
 
241
- | Target | Folder | Format |
242
- |--------|--------|--------|
243
- | ONNX FP32 | `onnx/` | `.onnx` |
244
- | TFLite INT8 | `tflite/` | `.tflite` |
245
- | NXP i.MX 95 (eIQ Neutron) | `imx95/` | `.imx95.tflite` |
246
- | NXP Ara240 | `ara240/` | `.dvm` |
247
- | RPi5 + Hailo-8L (13 TOPS) | `hailo/` | `.hailo8l.hef` |
248
- | NVIDIA Jetson (TensorRT) | `jetson/` | `.engine` |
249
- | Snapdragon (Qualcomm Hexagon HTP) | `qnn/` | `.htp-v81.qnn.onnx` |
250
-
251
- Each file embeds its `edgefirst.json` metadata (training session, dataset version, calibration artifact, converter chain), so a single download is sufficient for deployment — no sidecar configuration required.
252
 
253
  ---
254
 
 
25
  metrics:
26
  - name: "Box mAP@0.5 (Nano ONNX FP32)"
27
  type: map_50
28
+ value: 53.72
29
  - name: "Mask mAP@0.5-0.95 (Nano ONNX FP32)"
30
  type: map
31
+ value: 31.03
32
  - name: "Box mAP@0.5 (Small ONNX FP32)"
33
  type: map_50
34
+ value: 62.27
35
  - name: "Mask mAP@0.5-0.95 (Small ONNX FP32)"
36
  type: map
37
+ value: 36.17
38
  - name: "Box mAP@0.5 (Medium ONNX FP32)"
39
  type: map_50
40
+ value: 67.41
41
  - name: "Mask mAP@0.5-0.95 (Medium ONNX FP32)"
42
  type: map
43
+ value: 39.73
44
  ---
45
 
46
  # YOLO11 Segmentation — EdgeFirst Model Zoo
 
56
  Part of the [EdgeFirst Model Zoo](https://huggingface.co/spaces/EdgeFirst/Models). New here? **[Introducing the EdgeFirst Model Zoo](https://huggingface.co/blog/EdgeFirst/model-zoo-intro)** covers what is in the zoo, why every figure links to its validation session, and how to reproduce any of it on your own hardware. Want a platform, model or task we don't cover yet? [Vote on what we measure next](https://github.com/orgs/EdgeFirstAI/discussions/categories/polls).
57
 
58
  > [!TIP]
59
+ > **Training experiment**: [View on EdgeFirst Studio](https://edgefirst.studio/public/p-695/exp-c20/trainings) — dataset, training configuration, metrics, and exported artifacts.
60
+ > Rows badged **2026 Q2** come from the [2026 Q2 training experiment](https://edgefirst.studio/public/p-695/exp-886/trainings), whose weights the repository's `2026Q2` tag holds.
61
 
62
  > [!NOTE]
63
  > Architecture with C3k2 attention blocks.
 
70
 
71
  | Size | Params | GFLOPs | Box mAP@0.5 | Box mAP@0.5-0.95 | Mask mAP@0.5 | Mask mAP@0.5-0.95 | Source |
72
  |------|--------|--------|-------------|------------------|--------------|-------------------|--------|
73
+ | Nano | 2.6M | 6.5 | 53.72% | 38.48% | 50.50% | 31.03% | [v-1955](https://edgefirst.studio/public/validation/v-1955/details?mode=charts) |
74
+ | Small | 9.4M | 21.5 | 62.27% | 45.86% | 58.63% | 36.17% | [v-1956](https://edgefirst.studio/public/validation/v-1956/details?mode=charts) |
75
+ | Medium | 20.1M | 68.0 | 67.41% | 50.80% | 63.88% | 39.73% | [v-1959](https://edgefirst.studio/public/validation/v-1959/details?mode=charts) |
76
  | Large | 25.3M | 87.6 | — | — | — | — | — |
77
  | XLarge | 56.9M | 195.0 | — | — | — | — | — |
78
 
 
95
  - **Rows whose `Δ` cell reads `ref`** are the float reference runs each quantized/compiled measurement is graded against.
96
  - **Rows without a number** under the metric columns are validation sessions currently in progress, or a session not yet linked to its ONNX FP32 reference. The Studio Source link tracks the current status.
97
  - **Rows whose `Δ vs FP32` cell carries a ⚠** are below our accuracy expectations for that platform (more than 10 percentage points under the float reference). The numbers are real measurements on real hardware, reproducible from the linked Studio session, and we publish them as-is; we are investigating the results to make improvements, and the next snapshot of this card will reflect any recovered accuracy.
98
+ - **Edition badges.** A Source cell such as `v-XXXX · 2026 Q2` marks a result carried from an earlier EdgeFirst Perception Index edition: this configuration was not re-measured on the current weights, so the row keeps the measurement, Studio session and model artifact of the edition that produced it. Rows without a badge come from the current edition.
99
  - **Precision** varies by target: the ONNX reference rows are FP32; macOS CoreML and NVIDIA Jetson TensorRT run FP16; the NXP i.MX 8M Plus, NXP i.MX 95 Neutron, and Hailo NPUs run INT8. The NXP Ara240 DNPU runs a **mixed INT8/INT16** scheme — most of the model is INT8, with the box-regression path (and the ops feeding it) promoted to INT16 to improve localization accuracy.
100
  - **Decoder variants.** EdgeFirst ships three INT8 split-decoders — **smart**, **logical**, and **combined** (described under *Decode* below). Every one we have measured is listed; none is headlined over another, because which one wins is a property of the target, not of the variant. Full converter documentation: [EdgeFirst model conversion](https://doc.edgefirst.ai/latest/models/conversion/) — these are the converters used by this Model Zoo and the EdgeFirst Performance Index report.
101
  - **Runtime.** Which inference stack executed the model. It is a column of its own because one accelerator can be reached by more than one: on Qualcomm Hexagon HTP, `ONNX · QNN EP` is an ONNX Runtime EPContext binary while `LiteRT` is the LiteRT + QNN delegate path, and the two produce measurably different pipelines from the same weights on the same silicon. Compare rows that share a size, platform, precision, and decode to read that difference directly.
102
  - **Decode.** What the exported graph emits, and therefore what your application has to do with it. **Logical** keeps the head's outputs separate and named — boxes, scores, and for segmentation the mask prototypes and coefficients — so reading them needs no decoder beyond taking the tensors as they come. **Smart** cuts the graph further upstream, at a point chosen by dynamic range and sliceability: the operations that quantize worst are lifted out of the model and run outside it, which is what recovers the accuracy, and which is why it needs a decoder implementing that cut (in EdgeFirst deployments, the HAL provides it). **Combined** fuses the decode into the quantized graph — the standard upstream export. It is the least accurate of the three by construction, and the EdgeFirst converters do not emit it for quantized targets; rows appear only where one was measured. Compare rows sharing a size and platform to see what the cut buys on your target — on detection it is often small, on quantized segmentation masks it is usually decisive. A `—` means the run carries no quantized decode split at all (float ONNX / TensorRT / CoreML).
103
  - **Rows are not ranked.** Where a size and platform appear more than once, the rows are competing configurations — a different runtime, precision, decode variant, pipeline mode, or board — not duplicates. Only genuinely repeated measurements of the *same* configuration are collapsed, newest first.
104
+ - **Platform-label suffixes.** `(EVK)` / `(FRDM)` / `(Phytec)` / `(Verdin)` / `(Ezurio)` name the NXP i.MX development board a session ran on. `— latency` / `— throughput` mark the two pipeline configurations the NXP i.MX 95 Neutron and NXP Ara240 targets run: the **latency** pipeline runs inference serially for the lowest per-frame latency; the **throughput** pipeline runs multiple inference workers for the highest FPS, which raises per-frame latency in exchange. Rows with neither suffix run a single pipeline.
105
+ - **End-to-end (ms)** is the per-image latency from the moment a frame is available to the moment its results are ready: preprocess → inference → postprocess. Image acquisition (camera capture, or file load and JPEG decode), mask encoding and writing results to disk are excluded.
106
+ - **Realized FPS** is the *measured* steady-state throughput — the rate at which final results are actually delivered over the full validation pipeline. It normally exceeds `1000 / end-to-end` because the runtime overlaps stages across frames. Validation runs decode a JPEG per image and evaluate at a `0.001` confidence threshold to capture every detection for mAP; a live camera pipeline at a deployment threshold of `0.25–0.75` carries a lighter host and postprocess load.
107
+
108
+ | Size | Platform | Runtime | Decode | Box mAP@0.5 | Mask mAP@0.5-0.95 | Δ mask vs FP32 (pp) | End-to-end (ms) | Realized FPS | Source |
109
+ |------|----------|---------|--------|-------------|-------------------|---------------------|-----------------|--------------|--------|
110
+ | Nano | ONNX FP32 (AWS Graviton · 4-core) | ONNX | — | 53.30% | 30.74% | -0.01 | 370.16 | 11.4 | [v-ed7](https://edgefirst.studio/public/validation/v-ed7/details?mode=charts) · 2026 Q2 |
111
+ | Nano | ONNX FP32 (AWS Graviton4 · 48-core) | ONNX | — | 53.30% | 30.74% | -0.01 | 121.27 | 121.1 | [v-eed](https://edgefirst.studio/public/validation/v-eed/details?mode=charts) · 2026 Q2 |
112
+ | Nano | ONNX FP32 (AWS Graviton4 · 8-core) | ONNX | — | 53.30% | 30.74% | -0.01 | 303.74 | 26.9 | [v-ee4](https://edgefirst.studio/public/validation/v-ee4/details?mode=charts) · 2026 Q2 |
113
+ | Nano | ONNX FP32 (Intel Core i9-13900F · 32-core) | ONNX | — | 53.28% | 30.74% | -0.01 | 72.74 | 51.2 | [v-a4a](https://edgefirst.studio/public/validation/v-a4a/details?mode=charts) · 2026 Q2 |
114
+ | Nano | ONNX FP32 (Intel Xeon Platinum 8488C · 24-core) | ONNX | — | 53.28% | 30.74% | -0.01 | 129.79 | 105.0 | [v-eaf](https://edgefirst.studio/public/validation/v-eaf/details?mode=charts) · 2026 Q2 |
115
+ | Nano | ONNX FP32 (Intel Xeon Platinum 8488C · 4-core) | ONNX | — | 53.28% | 30.74% | -0.01 | 255.29 | 33.2 | [v-ea6](https://edgefirst.studio/public/validation/v-ea6/details?mode=charts) · 2026 Q2 |
116
+ | Nano | ONNX FP32 (CUDA · A10G) | ONNX | — | 53.27% | 30.75% | ref | 35.87 | 129.6 | [v-e97](https://edgefirst.studio/public/validation/v-e97/details?mode=charts) · 2026 Q2 |
117
+ | Nano | ONNX FP32 (CUDA · RTX 4060) | ONNX | — | 53.28% | 30.75% | +0.00 | 17.81 | 244.6 | [v-a8c](https://edgefirst.studio/public/validation/v-a8c/details?mode=charts) · 2026 Q2 |
118
+ | Nano | ONNX FP16 (CUDA · RTX 4060) | ONNX | — | 53.29% | 30.74% | -0.01 | 16.29 | 305.4 | [v-aa1](https://edgefirst.studio/public/validation/v-aa1/details?mode=charts) · 2026 Q2 |
119
+ | Nano | ONNX FP32 (CUDA · RTX 3090) | ONNX | — | 53.72% | 31.03% | ref | 25.85 | 198.0 | [v-1955](https://edgefirst.studio/public/validation/v-1955/details?mode=charts) |
120
+ | Nano | ONNX FP16 (CUDA · RTX 3090) | ONNX | — | 53.71% | 31.02% | -0.01 | 22.17 | 249.8 | [v-19b6](https://edgefirst.studio/public/validation/v-19b6/details?mode=charts) |
121
+ | Nano | Apple M2 Max — CoreML Neural Engine (FP16) | ONNX | — | 52.89% | 30.50% | -0.25 | 10.24 | 349.9 | [v-9f0](https://edgefirst.studio/public/validation/v-9f0/details?mode=charts) · 2026 Q2 |
122
+ | Nano | Apple M2 Max — CoreML Metal GPU (FP16) | ONNX | — | 51.85% | 29.94% | -0.81 | 14.43 | 290.7 | [v-739](https://edgefirst.studio/public/validation/v-739/details?mode=charts) · 2026 Q2 |
123
+ | Nano | Apple M2 Max — CoreML CPU (FP16) | ONNX | — | 52.85% | 30.51% | -0.24 | 26.53 | 90.6 | [v-9f1](https://edgefirst.studio/public/validation/v-9f1/details?mode=charts) · 2026 Q2 |
124
+ | Nano | Apple iPhone 17 Pro — CoreML Neural Engine (FP16) | ONNX | — | 52.89% | 30.49% | -0.26 | 23.37 | 143.3 | [v-f67](https://edgefirst.studio/public/validation/v-f67/details?mode=charts) · 2026 Q2 |
125
+ | Nano | Apple iPhone 17 Pro — CoreML Metal GPU (FP16) | ONNX | — | 52.91% | 30.52% | -0.23 | 34.65 | 100.7 | [v-f66](https://edgefirst.studio/public/validation/v-f66/details?mode=charts) · 2026 Q2 |
126
+ | Nano | Apple iPhone 17 Pro — CoreML CPU (FP16) | ONNX | — | 52.85% | 30.48% | -0.27 | 43.99 | 65.1 | [v-f64](https://edgefirst.studio/public/validation/v-f64/details?mode=charts) · 2026 Q2 |
127
+ | Nano | Apple iPhone 15 Pro — CoreML Neural Engine (FP16) | ONNX | — | 52.90% | 30.48% | -0.27 | 29.62 | 107.7 | [v-f25](https://edgefirst.studio/public/validation/v-f25/details?mode=charts) · 2026 Q2 |
128
+ | Nano | Apple iPhone 15 Pro — CoreML Metal GPU (FP16) | ONNX | — | 52.91% | 30.53% | -0.22 | 57.96 | 63.1 | [v-f26](https://edgefirst.studio/public/validation/v-f26/details?mode=charts) · 2026 Q2 |
129
+ | Nano | Apple iPhone 15 Pro — CoreML CPU (FP16) | ONNX | — | 52.85% | 30.50% | -0.25 | 59.55 | 48.3 | [v-f00](https://edgefirst.studio/public/validation/v-f00/details?mode=charts) · 2026 Q2 |
130
+ | Nano | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT8) | ONNX · QNN EP | Smart | 48.69% | 27.48% | -3.27 | 35.27 | 97.8 | [v-10a9](https://edgefirst.studio/public/validation/v-10a9/details?mode=charts) · 2026 Q2 |
131
+ | Nano | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT8) | ONNX · QNN EP | Logical | 37.27% | 18.48% | -12.27 | 10.68 | 342.4 | [v-10ac](https://edgefirst.studio/public/validation/v-10ac/details?mode=charts) · 2026 Q2 |
132
+ | Nano | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT16) | ONNX · QNN EP | Smart | 51.20% | 29.21% | -1.54 | 43.92 | 87.8 | [v-10a6](https://edgefirst.studio/public/validation/v-10a6/details?mode=charts) · 2026 Q2 |
133
+ | Nano | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT16) | ONNX · QNN EP | Logical | 50.14% | 11.13% | -19.62 | 23.62 | 151.3 | [v-10a7](https://edgefirst.studio/public/validation/v-10a7/details?mode=charts) · 2026 Q2 |
134
+ | Nano | Samsung Galaxy S26 Ultra — CPU | LiteRT | Smart | 50.86% | 28.96% | -1.79 | 137.77 | 53.1 | [v-fc5](https://edgefirst.studio/public/validation/v-fc5/details?mode=charts) · 2026 Q2 |
135
+ | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency | LiteRT | Logical | 47.86% | 27.18% | -3.85 | 354.57 | 4.3 | [v-19de](https://edgefirst.studio/public/validation/v-19de/details?mode=charts) |
136
+ | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Combined | 39.36% | 22.58% | -8.45 | 127.42 | 8.4 | [v-1a00](https://edgefirst.studio/public/validation/v-1a00/details?mode=charts) |
137
+ | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Logical | 47.86% | 27.18% | -3.85 | 135.28 | 8.3 | [v-1a15](https://edgefirst.studio/public/validation/v-1a15/details?mode=charts) |
138
+ | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Smart | 49.44% | 27.83% | -3.20 | 166.11 | 8.8 | [v-1a29](https://edgefirst.studio/public/validation/v-1a29/details?mode=charts) |
139
+ | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (Phytec) — latency | LiteRT | Logical | 47.74% | 27.09% | -3.94 | 140.28 | 8.8 | [v-19a9](https://edgefirst.studio/public/validation/v-19a9/details?mode=charts) |
140
+ | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) — latency | LiteRT | Logical | 37.52% | 21.48% | -9.27 | 132.64 | 8.2 | [v-c86](https://edgefirst.studio/public/validation/v-c86/details?mode=charts) · 2026 Q2 |
141
+ | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) — throughput | LiteRT | Smart | 48.41% | 27.17% | -3.58 | 185.55 | 8.5 | [v-c85](https://edgefirst.studio/public/validation/v-c85/details?mode=charts) · 2026 Q2 |
142
+ | Nano | NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency | LiteRT | Smart | 51.29% | 29.24% | -1.79 | 108.53 | 23.5 | [v-169c](https://edgefirst.studio/public/validation/v-169c/details?mode=charts) |
143
+ | Nano | NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput | LiteRT | Smart | 51.29% | 29.24% | -1.79 | 205.65 | 25.4 | [v-193c](https://edgefirst.studio/public/validation/v-193c/details?mode=charts) |
144
+ | Nano | NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency | LiteRT | Smart | 51.22% | 29.08% | -1.95 | 100.98 | 23.5 | [v-184e](https://edgefirst.studio/public/validation/v-184e/details?mode=charts) |
145
+ | Nano | NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency | LiteRT | Logical | 49.57% | 28.45% | -2.58 | 75.82 | 16.8 | [v-184a](https://edgefirst.studio/public/validation/v-184a/details?mode=charts) |
146
+ | Nano | NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency | LiteRT | Combined | 40.89% | 23.70% | -7.33 | 72.50 | 16.3 | [v-1847](https://edgefirst.studio/public/validation/v-1847/details?mode=charts) |
147
+ | Nano | NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput | LiteRT | Smart | 51.22% | 29.08% | -1.95 | 184.40 | 25.2 | [v-184f](https://edgefirst.studio/public/validation/v-184f/details?mode=charts) |
148
+ | Nano | NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput | LiteRT | Logical | 49.57% | 28.45% | -2.58 | 156.42 | 34.3 | [v-184d](https://edgefirst.studio/public/validation/v-184d/details?mode=charts) |
149
+ | Nano | NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput | LiteRT | Combined | 40.89% | 23.70% | -7.33 | 122.31 | 40.6 | [v-1848](https://edgefirst.studio/public/validation/v-1848/details?mode=charts) |
150
+ | Nano | NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput | LiteRT | Smart | 51.22% | 29.08% | -1.95 | 183.90 | 25.8 | [v-1859](https://edgefirst.studio/public/validation/v-1859/details?mode=charts) |
151
+ | Nano | NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency | LiteRT | Smart | 51.29% | 29.24% | -1.79 | 114.65 | 22.6 | [v-169e](https://edgefirst.studio/public/validation/v-169e/details?mode=charts) |
152
+ | Nano | NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput | LiteRT | Smart | 51.29% | 29.24% | -1.79 | 215.54 | 24.2 | [v-1953](https://edgefirst.studio/public/validation/v-1953/details?mode=charts) |
153
+ | Nano | NXP Ara240 (FRDM) — latency | Ara DVM | Smart | 50.71% | 28.88% | -2.15 | 45.13 | 49.2 | [v-165a](https://edgefirst.studio/public/validation/v-165a/details?mode=charts) |
154
+ | Nano | NXP Ara240 (FRDM) — throughput | Ara DVM | Smart | 50.72% | 28.89% | -2.14 | 56.28 | 53.2 | [v-18ea](https://edgefirst.studio/public/validation/v-18ea/details?mode=charts) |
155
+ | Nano | Raspberry Pi 5 + Hailo-8L NPU | Hailo HEF | — | 51.56% | 29.45% | -1.58 | 35.50 | 40.4 | [v-1576](https://edgefirst.studio/public/validation/v-1576/details?mode=charts) |
156
+ | Nano | NVIDIA Jetson Orin Nano (TensorRT FP16) | TensorRT | — | 53.69% | 31.01% | -0.02 | 67.94 | 98.4 | [v-158b](https://edgefirst.studio/public/validation/v-158b/details?mode=charts) |
157
+ | Nano | NVIDIA Jetson Orin Nano (TensorRT INT8) | TensorRT | — | 52.36% | 29.82% | -1.21 | 68.92 | 93.8 | [v-158d](https://edgefirst.studio/public/validation/v-158d/details?mode=charts) |
158
+ | Nano | Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8) | ONNX · QNN EP | Smart | 51.67% | 29.56% | -1.47 | 22.60 | 169.9 | [v-15f3](https://edgefirst.studio/public/validation/v-15f3/details?mode=charts) |
159
+ | Nano | Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8) | ONNX · QNN EP | Logical | 50.25% | 28.81% | -2.22 | 16.37 | 244.5 | [v-15ef](https://edgefirst.studio/public/validation/v-15ef/details?mode=charts) |
160
+ | Nano | Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16) | ONNX · QNN EP | Smart | 53.26% | 30.70% | -0.33 | 30.90 | 140.4 | [v-15ff](https://edgefirst.studio/public/validation/v-15ff/details?mode=charts) |
161
+ | Small | ONNX FP32 (AWS Graviton · 4-core) | ONNX | — | 61.74% | 35.71% | -0.01 | 975.33 | 4.2 | [v-ed8](https://edgefirst.studio/public/validation/v-ed8/details?mode=charts) · 2026 Q2 |
162
+ | Small | ONNX FP32 (AWS Graviton4 · 8-core) | ONNX | — | 61.74% | 35.71% | -0.01 | 800.80 | 10.0 | [v-ee8](https://edgefirst.studio/public/validation/v-ee8/details?mode=charts) · 2026 Q2 |
163
+ | Small | ONNX FP32 (AWS Graviton4 · 48-core) | ONNX | — | 61.74% | 35.71% | -0.01 | 275.18 | 48.7 | [v-ee1](https://edgefirst.studio/public/validation/v-ee1/details?mode=charts) · 2026 Q2 |
164
+ | Small | ONNX FP32 (Intel Core i9-13900F · 32-core) | ONNX | — | 61.72% | 35.73% | +0.01 | 146.54 | 21.5 | [v-a51](https://edgefirst.studio/public/validation/v-a51/details?mode=charts) · 2026 Q2 |
165
+ | Small | ONNX FP32 (Intel Xeon Platinum 8488C · 24-core) | ONNX | — | 61.72% | 35.72% | +0.00 | 263.35 | 53.1 | [v-eb2](https://edgefirst.studio/public/validation/v-eb2/details?mode=charts) · 2026 Q2 |
166
+ | Small | ONNX FP32 (Intel Xeon Platinum 8488C · 4-core) | ONNX | — | 61.72% | 35.72% | +0.00 | 480.32 | 17.0 | [v-eae](https://edgefirst.studio/public/validation/v-eae/details?mode=charts) · 2026 Q2 |
167
+ | Small | ONNX FP32 (CUDA · A10G) | ONNX | — | 61.72% | 35.72% | ref | 34.51 | 143.8 | [v-e98](https://edgefirst.studio/public/validation/v-e98/details?mode=charts) · 2026 Q2 |
168
+ | Small | ONNX FP32 (CUDA · RTX 4060) | ONNX | — | 61.72% | 35.72% | +0.00 | 27.11 | 153.4 | [v-a93](https://edgefirst.studio/public/validation/v-a93/details?mode=charts) · 2026 Q2 |
169
+ | Small | ONNX FP16 (CUDA · RTX 4060) | ONNX | — | 61.69% | 35.73% | +0.01 | 21.47 | 224.4 | [v-aa8](https://edgefirst.studio/public/validation/v-aa8/details?mode=charts) · 2026 Q2 |
170
+ | Small | ONNX FP32 (CUDA · RTX 3090) | ONNX | — | 62.27% | 36.17% | ref | 26.32 | 200.2 | [v-1956](https://edgefirst.studio/public/validation/v-1956/details?mode=charts) |
171
+ | Small | ONNX FP16 (CUDA · RTX 3090) | ONNX | — | 62.22% | 36.18% | +0.01 | 21.40 | 266.8 | [v-19b7](https://edgefirst.studio/public/validation/v-19b7/details?mode=charts) |
172
+ | Small | Apple M2 Max — CoreML Neural Engine (FP16) | ONNX | — | 60.16% | 34.89% | -0.83 | 11.03 | 253.7 | [v-730](https://edgefirst.studio/public/validation/v-730/details?mode=charts) · 2026 Q2 |
173
+ | Small | Apple M2 Max — CoreML Metal GPU (FP16) | ONNX | — | 61.03% | 35.29% | -0.43 | 27.02 | 134.1 | [v-9f2](https://edgefirst.studio/public/validation/v-9f2/details?mode=charts) · 2026 Q2 |
174
+ | Small | Apple M2 Max — CoreML CPU (FP16) | ONNX | — | 61.03% | 35.29% | -0.43 | 45.54 | 48.1 | [v-9f3](https://edgefirst.studio/public/validation/v-9f3/details?mode=charts) · 2026 Q2 |
175
+ | Small | Apple iPhone 17 Pro — CoreML Neural Engine (FP16) | ONNX | — | 60.98% | 35.34% | -0.38 | 22.17 | 141.3 | [v-f6f](https://edgefirst.studio/public/validation/v-f6f/details?mode=charts) · 2026 Q2 |
176
+ | Small | Apple iPhone 17 Pro — CoreML Metal GPU (FP16) | ONNX | — | 61.06% | 35.32% | -0.40 | 39.92 | 65.9 | [v-f70](https://edgefirst.studio/public/validation/v-f70/details?mode=charts) · 2026 Q2 |
177
+ | Small | Apple iPhone 17 Pro — CoreML CPU (FP16) | ONNX | — | 61.03% | 35.30% | -0.42 | 78.70 | 28.9 | [v-f6e](https://edgefirst.studio/public/validation/v-f6e/details?mode=charts) · 2026 Q2 |
178
+ | Small | Apple iPhone 15 Pro — CoreML Neural Engine (FP16) | ONNX | — | 60.98% | 35.35% | -0.37 | 34.40 | 109.1 | [v-f3a](https://edgefirst.studio/public/validation/v-f3a/details?mode=charts) · 2026 Q2 |
179
+ | Small | Apple iPhone 15 Pro — CoreML Metal GPU (FP16) | ONNX | — | 61.07% | 35.33% | -0.39 | 76.93 | 28.5 | [v-f3e](https://edgefirst.studio/public/validation/v-f3e/details?mode=charts) · 2026 Q2 |
180
+ | Small | Apple iPhone 15 Pro — CoreML CPU (FP16) | ONNX | — | 61.02% | 35.30% | -0.42 | 89.69 | 25.7 | [v-f03](https://edgefirst.studio/public/validation/v-f03/details?mode=charts) · 2026 Q2 |
181
+ | Small | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT8) | ONNX · QNN EP | Smart | 59.73% | 34.12% | -1.60 | 30.53 | 120.1 | [v-10b2](https://edgefirst.studio/public/validation/v-10b2/details?mode=charts) · 2026 Q2 |
182
+ | Small | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT8) | ONNX · QNN EP | Logical | 46.27% | 22.39% | -13.33 | 11.76 | 331.4 | [v-10b4](https://edgefirst.studio/public/validation/v-10b4/details?mode=charts) · 2026 Q2 |
183
+ | Small | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT16) | ONNX · QNN EP | Smart | 61.09% | 35.14% | -0.58 | 43.16 | 87.0 | [v-10ad](https://edgefirst.studio/public/validation/v-10ad/details?mode=charts) · 2026 Q2 |
184
+ | Small | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT16) | ONNX · QNN EP | Logical | 60.16% | 12.52% | -23.20 | 27.14 | 141.3 | [v-10b0](https://edgefirst.studio/public/validation/v-10b0/details?mode=charts) · 2026 Q2 |
185
+ | Small | Samsung Galaxy S26 Ultra — CPU | LiteRT | Smart | 60.56% | 34.69% | -1.03 | 204.94 | 32.8 | [v-100c](https://edgefirst.studio/public/validation/v-100c/details?mode=charts) · 2026 Q2 |
186
+ | Small | NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency | LiteRT | Logical | 44.40% | 23.56% | -12.61 | 463.64 | 4.0 | [v-1a23](https://edgefirst.studio/public/validation/v-1a23/details?mode=charts) |
187
+ | Small | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Combined | 35.45% | 18.84% | -17.33 | 219.00 | 4.8 | [v-1a36](https://edgefirst.studio/public/validation/v-1a36/details?mode=charts) |
188
+ | Small | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Logical | 44.40% | 23.56% | -12.61 | 227.27 | 4.7 | [v-1a4b](https://edgefirst.studio/public/validation/v-1a4b/details?mode=charts) |
189
+ | Small | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Smart | 46.40% | 24.33% | -11.84 ⚠ | 252.08 | 4.9 | [v-1a6c](https://edgefirst.studio/public/validation/v-1a6c/details?mode=charts) |
190
+ | Small | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) — latency | LiteRT | Logical | 44.46% | 23.62% | -12.55 | 243.50 | 4.4 | [v-1a05](https://edgefirst.studio/public/validation/v-1a05/details?mode=charts) |
191
+ | Small | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) — throughput | LiteRT | Smart | 42.80% | 22.19% | -13.53 ⚠ | 268.86 | 4.8 | [v-c96](https://edgefirst.studio/public/validation/v-c96/details?mode=charts) · 2026 Q2 |
192
+ | Small | NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — latency | LiteRT | Smart | 60.72% | 34.96% | -1.21 | 100.63 | 15.4 | [v-16ad](https://edgefirst.studio/public/validation/v-16ad/details?mode=charts) |
193
+ | Small | NXP i.MX 95 + eIQ Neutron NPU (Ezurio) — throughput | LiteRT | Smart | 60.72% | 34.96% | -1.21 | 229.36 | 23.4 | [v-194d](https://edgefirst.studio/public/validation/v-194d/details?mode=charts) |
194
+ | Small | NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency | LiteRT | Smart | 60.59% | 34.85% | -1.32 | 102.88 | 15.1 | [v-185e](https://edgefirst.studio/public/validation/v-185e/details?mode=charts) |
195
+ | Small | NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency | LiteRT | Logical | 58.32% | 33.91% | -2.26 | 107.37 | 10.5 | [v-1858](https://edgefirst.studio/public/validation/v-1858/details?mode=charts) |
196
+ | Small | NXP i.MX 95 + eIQ Neutron NPU (EVK) — latency | LiteRT | Combined | 47.69% | 28.14% | -8.03 | 108.27 | 10.1 | [v-1851](https://edgefirst.studio/public/validation/v-1851/details?mode=charts) |
197
+ | Small | NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput | LiteRT | Smart | 60.59% | 34.85% | -1.32 | 224.21 | 23.4 | [v-1863](https://edgefirst.studio/public/validation/v-1863/details?mode=charts) |
198
+ | Small | NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput | LiteRT | Logical | 58.32% | 33.91% | -2.26 | 194.06 | 23.4 | [v-185d](https://edgefirst.studio/public/validation/v-185d/details?mode=charts) |
199
+ | Small | NXP i.MX 95 + eIQ Neutron NPU (EVK) — throughput | LiteRT | Combined | 47.69% | 28.14% | -8.03 | 174.84 | 24.7 | [v-1856](https://edgefirst.studio/public/validation/v-1856/details?mode=charts) |
200
+ | Small | NXP i.MX 95 + eIQ Neutron NPU (Phytec) — throughput | LiteRT | Smart | 60.59% | 34.85% | -1.32 | 221.56 | 23.4 | [v-1853](https://edgefirst.studio/public/validation/v-1853/details?mode=charts) |
201
+ | Small | NXP i.MX 95 + eIQ Neutron NPU (Verdin) — latency | LiteRT | Smart | 60.72% | 34.96% | -1.21 | 106.49 | 14.7 | [v-16af](https://edgefirst.studio/public/validation/v-16af/details?mode=charts) |
202
+ | Small | NXP i.MX 95 + eIQ Neutron NPU (Verdin) — throughput | LiteRT | Smart | 60.72% | 34.96% | -1.21 | 239.68 | 22.4 | [v-1965](https://edgefirst.studio/public/validation/v-1965/details?mode=charts) |
203
+ | Small | NXP Ara240 (FRDM) — latency | Ara DVM | Smart | 60.23% | 35.26% | -0.91 | 40.74 | 51.5 | [v-154b](https://edgefirst.studio/public/validation/v-154b/details?mode=charts) |
204
+ | Small | NXP Ara240 (FRDM) — throughput | Ara DVM | Smart | 60.24% | 35.27% | -0.90 | 48.62 | 67.1 | [v-18ec](https://edgefirst.studio/public/validation/v-18ec/details?mode=charts) |
205
+ | Small | Raspberry Pi 5 + Hailo-8L NPU | Hailo HEF | — | 60.62% | 34.91% | -1.26 | 51.54 | 23.8 | [v-157c](https://edgefirst.studio/public/validation/v-157c/details?mode=charts) |
206
+ | Small | NVIDIA Jetson Orin Nano (TensorRT FP16) | TensorRT | — | 62.20% | 36.15% | -0.02 | 66.47 | 106.8 | [v-1592](https://edgefirst.studio/public/validation/v-1592/details?mode=charts) |
207
+ | Small | NVIDIA Jetson Orin Nano (TensorRT INT8) | TensorRT | — | 61.33% | 35.64% | -0.53 | 59.17 | 114.9 | [v-1597](https://edgefirst.studio/public/validation/v-1597/details?mode=charts) |
208
+ | Small | Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8) | ONNX · QNN EP | Smart | 60.78% | 35.25% | -0.92 | 22.83 | 184.5 | [v-161e](https://edgefirst.studio/public/validation/v-161e/details?mode=charts) |
209
+ | Small | Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8) | ONNX · QNN EP | Logical | 58.54% | 34.05% | -2.12 | 19.07 | 266.1 | [v-1618](https://edgefirst.studio/public/validation/v-1618/details?mode=charts) |
210
+ | Small | Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16) | ONNX · QNN EP | Smart | 62.17% | 36.16% | -0.01 | 36.03 | 141.4 | [v-1623](https://edgefirst.studio/public/validation/v-1623/details?mode=charts) |
211
+ | Medium | ONNX FP32 (AWS Graviton · 4-core) | ONNX | — | 67.13% | 39.45% | +0.02 | 3030.67 | 1.3 | [v-ed9](https://edgefirst.studio/public/validation/v-ed9/details?mode=charts) · 2026 Q2 |
212
+ | Medium | ONNX FP32 (AWS Graviton4 · 8-core) | ONNX | — | 67.13% | 39.45% | +0.02 | 2455.22 | 3.3 | [v-eea](https://edgefirst.studio/public/validation/v-eea/details?mode=charts) · 2026 Q2 |
213
+ | Medium | ONNX FP32 (AWS Graviton4 · 48-core) | ONNX | — | 67.13% | 39.45% | +0.02 | 849.14 | 15.3 | [v-ee3](https://edgefirst.studio/public/validation/v-ee3/details?mode=charts) · 2026 Q2 |
214
+ | Medium | ONNX FP32 (Intel Core i9-13900F · 32-core) | ONNX | — | 67.10% | 39.43% | +0.00 | 380.55 | 7.9 | [v-a58](https://edgefirst.studio/public/validation/v-a58/details?mode=charts) · 2026 Q2 |
215
+ | Medium | ONNX FP32 (Intel Xeon Platinum 8488C · 24-core) | ONNX | — | 67.10% | 39.43% | +0.00 | 705.60 | 18.6 | [v-eb5](https://edgefirst.studio/public/validation/v-eb5/details?mode=charts) · 2026 Q2 |
216
+ | Medium | ONNX FP32 (Intel Xeon Platinum 8488C · 4-core) | ONNX | — | 67.10% | 39.43% | +0.00 | 1747.81 | 4.6 | [v-eb0](https://edgefirst.studio/public/validation/v-eb0/details?mode=charts) · 2026 Q2 |
217
+ | Medium | ONNX FP32 (CUDA · A10G) | ONNX | — | 67.11% | 39.43% | ref | 46.19 | 105.1 | [v-e9a](https://edgefirst.studio/public/validation/v-e9a/details?mode=charts) · 2026 Q2 |
218
+ | Medium | ONNX FP32 (CUDA · RTX 4060) | ONNX | — | 67.11% | 39.42% | -0.01 | 60.45 | 64.2 | [v-a9a](https://edgefirst.studio/public/validation/v-a9a/details?mode=charts) · 2026 Q2 |
219
+ | Medium | ONNX FP16 (CUDA · RTX 4060) | ONNX | — | 67.10% | 39.44% | +0.01 | 38.19 | 114.2 | [v-aaf](https://edgefirst.studio/public/validation/v-aaf/details?mode=charts) · 2026 Q2 |
220
+ | Medium | ONNX FP32 (CUDA · RTX 3090) | ONNX | — | 67.41% | 39.73% | ref | 38.02 | 129.8 | [v-1959](https://edgefirst.studio/public/validation/v-1959/details?mode=charts) |
221
+ | Medium | ONNX FP16 (CUDA · RTX 3090) | ONNX | — | 67.40% | 39.75% | +0.02 | 26.51 | 204.9 | [v-19b8](https://edgefirst.studio/public/validation/v-19b8/details?mode=charts) |
222
+ | Medium | Apple M2 Max — CoreML Neural Engine (FP16) | ONNX | — | 65.03% | 38.29% | -1.14 | 28.75 | 79.3 | [v-7ff](https://edgefirst.studio/public/validation/v-7ff/details?mode=charts) · 2026 Q2 |
223
+ | Medium | Apple M2 Max — CoreML Metal GPU (FP16) | ONNX | — | 65.99% | 38.70% | -0.73 | 67.66 | 47.2 | [v-9e1](https://edgefirst.studio/public/validation/v-9e1/details?mode=charts) · 2026 Q2 |
224
+ | Medium | Apple M2 Max — CoreML CPU (FP16) | ONNX | — | 65.96% | 38.67% | -0.76 | 96.03 | 21.7 | [v-9e0](https://edgefirst.studio/public/validation/v-9e0/details?mode=charts) · 2026 Q2 |
225
+ | Medium | Apple iPhone 17 Pro — CoreML Neural Engine (FP16) | ONNX | — | 65.98% | 38.80% | -0.63 | 39.39 | 58.0 | [v-f77](https://edgefirst.studio/public/validation/v-f77/details?mode=charts) · 2026 Q2 |
226
+ | Medium | Apple iPhone 17 Pro — CoreML Metal GPU (FP16) | ONNX | — | 66.00% | 38.72% | -0.71 | 92.36 | 22.6 | [v-f78](https://edgefirst.studio/public/validation/v-f78/details?mode=charts) · 2026 Q2 |
227
+ | Medium | Apple iPhone 17 Pro — CoreML CPU (FP16) | ONNX | — | 65.98% | 38.66% | -0.77 | 186.56 | 11.2 | [v-f7a](https://edgefirst.studio/public/validation/v-f7a/details?mode=charts) · 2026 Q2 |
228
+ | Medium | Apple iPhone 15 Pro — CoreML Neural Engine (FP16) | ONNX | — | 65.98% | 38.80% | -0.63 | 48.07 | 48.5 | [v-f4c](https://edgefirst.studio/public/validation/v-f4c/details?mode=charts) · 2026 Q2 |
229
+ | Medium | Apple iPhone 15 Pro — CoreML Metal GPU (FP16) | ONNX | — | 66.00% | 38.71% | -0.72 | 223.09 | 9.2 | [v-f61](https://edgefirst.studio/public/validation/v-f61/details?mode=charts) · 2026 Q2 |
230
+ | Medium | Apple iPhone 15 Pro — CoreML CPU (FP16) | ONNX | — | 65.98% | 38.68% | -0.75 | 225.36 | 9.3 | [v-f06](https://edgefirst.studio/public/validation/v-f06/details?mode=charts) · 2026 Q2 |
231
+ | Medium | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT8) | ONNX · QNN EP | Smart | 65.06% | 38.08% | -1.35 | 33.75 | 115.2 | [v-10ba](https://edgefirst.studio/public/validation/v-10ba/details?mode=charts) · 2026 Q2 |
232
+ | Medium | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT8) | ONNX · QNN EP | Logical | 49.44% | 28.14% | -11.29 | 15.95 | 228.2 | [v-10bc](https://edgefirst.studio/public/validation/v-10bc/details?mode=charts) · 2026 Q2 |
233
+ | Medium | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT16) | ONNX · QNN EP | Smart | 66.15% | 38.72% | -0.71 | 49.52 | 76.1 | [v-10b6](https://edgefirst.studio/public/validation/v-10b6/details?mode=charts) · 2026 Q2 |
234
+ | Medium | Samsung Galaxy S26 Ultra — Qualcomm Hexagon NPU (INT16) | ONNX · QNN EP | Logical | 65.18% | 13.50% | -25.93 | 38.01 | 83.2 | [v-10b8](https://edgefirst.studio/public/validation/v-10b8/details?mode=charts) · 2026 Q2 |
235
+ | Medium | Samsung Galaxy S26 Ultra — CPU | LiteRT | Smart | 65.85% | 38.64% | -0.79 | 497.46 | 12.5 | [v-1021](https://edgefirst.studio/public/validation/v-1021/details?mode=charts) · 2026 Q2 |
236
+ | Medium | NXP i.MX 8M Plus + VeriSilicon NPU (Ezurio) — latency | LiteRT | Logical | 54.51% | 30.89% | -8.84 | 685.64 | 2.5 | [v-1a71](https://edgefirst.studio/public/validation/v-1a71/details?mode=charts) |
237
+ | Medium | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Combined | 43.22% | 24.80% | -14.93 | 414.21 | 2.5 | [v-1a78](https://edgefirst.studio/public/validation/v-1a78/details?mode=charts) |
238
+ | Medium | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Logical | 54.51% | 30.89% | -8.84 | 418.98 | 2.5 | [v-1a9c](https://edgefirst.studio/public/validation/v-1a9c/details?mode=charts) |
239
+ | Medium | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | LiteRT | Smart | 57.40% | 32.12% | -7.61 ⚠ | 440.83 | 2.5 | [v-1ac5](https://edgefirst.studio/public/validation/v-1ac5/details?mode=charts) |
240
+ | Medium | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) — latency | LiteRT | Logical | 54.61% | 30.96% | -8.77 | 450.84 | 2.3 | [v-1a59](https://edgefirst.studio/public/validation/v-1a59/details?mode=charts) |
241
+ | Medium | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) — throughput | LiteRT | Smart | 54.71% | 30.31% | -9.12 ⚠ | 458.32 | 2.5 | [v-ca8](https://edgefirst.studio/public/validation/v-ca8/details?mode=charts) · 2026 Q2 |
242
+ | Medium | NXP Ara240 (FRDM) — latency | Ara DVM | Smart | 65.06% | 38.68% | -1.05 | 62.44 | 23.7 | [v-1567](https://edgefirst.studio/public/validation/v-1567/details?mode=charts) |
243
+ | Medium | NXP Ara240 (FRDM) — throughput | Ara DVM | Smart | 65.04% | 38.66% | -1.07 | 54.55 | 28.9 | [v-18f1](https://edgefirst.studio/public/validation/v-18f1/details?mode=charts) |
244
+ | Medium | Raspberry Pi 5 + Hailo-8L NPU | Hailo HEF | — | 65.44% | 38.62% | -1.11 | 94.36 | 11.8 | [v-1585](https://edgefirst.studio/public/validation/v-1585/details?mode=charts) |
245
+ | Medium | NVIDIA Jetson Orin Nano (TensorRT FP16) | TensorRT | — | 67.41% | 39.74% | +0.01 | 86.16 | 55.5 | [v-159c](https://edgefirst.studio/public/validation/v-159c/details?mode=charts) |
246
+ | Medium | NVIDIA Jetson Orin Nano (TensorRT INT8) | TensorRT | — | 66.51% | 39.20% | -0.53 | 65.39 | 89.4 | [v-15a1](https://edgefirst.studio/public/validation/v-15a1/details?mode=charts) |
247
+ | Medium | Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8) | ONNX · QNN EP | Smart | 66.23% | 38.78% | -0.95 | 31.39 | 169.9 | [v-1652](https://edgefirst.studio/public/validation/v-1652/details?mode=charts) |
248
+ | Medium | Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT8) | ONNX · QNN EP | Logical | 63.21% | 37.66% | -2.07 | 22.89 | 167.2 | [v-164d](https://edgefirst.studio/public/validation/v-164d/details?mode=charts) |
249
+ | Medium | Qualcomm Dragonwing IQ-9075 — Hexagon NPU (INT16) | ONNX · QNN EP | Smart | 67.28% | 39.60% | -0.13 | 47.17 | 63.5 | [v-1655](https://edgefirst.studio/public/validation/v-1655/details?mode=charts) |
250
 
251
  > **⚠ Below expectations — under investigation.** The rows marked ⚠ above measure more than 10 percentage points below the same training session's float reference: the model accuracy on that platform is below our expectations. We publish the measured numbers rather than hiding them, and we are investigating the results to make improvements — the next snapshot of this card will reflect any recovered accuracy.
252
 
 
261
  2. **EdgeFirst Studio** receives the metrics, predictions, charts and trace, and publishes them as the `v-XXXX` validation session every row above cites. Studio is where results are published, compared and browsed — it is not where they are computed.
262
  3. **EdgeFirst HAL** ([open source](https://github.com/EdgeFirstAI/hal)) provides the hardware-accelerated preprocessing and post-decoding primitives used at both validation and deployment time, so the timings measured here reflect the same accelerated paths a production runtime would take.
263
 
264
+ End-to-end latency runs from the moment a frame is available to the moment its results are ready — preprocessing, inference, and postprocessing. Image acquisition (camera capture, or file load and JPEG decode), mask encoding and writing results to disk are excluded. The per-stage breakdown of every row is on its linked Studio session.
265
 
266
+ **Realized FPS** is the *measured* steady-state rate at which final results are emitted, measured directly from the profiler's per-frame result-emission timestamps over the steady-state stream — trace-independent; the Perfetto trace's own FPS is used only as a fallback on sessions where that scalar isn't available. It generally exceeds `1000 / end-to-end` because the runtime overlaps stages across frames.
267
 
268
  See [EdgeFirst Studio](https://edgefirst.studio) for the full validation pipeline.
269
 
 
273
 
274
  Artifacts are organized by deployment target. Each model file embeds the EdgeFirst `edgefirst.json` metadata (training session, dataset version, calibration artifact, converter chain) so a single file is sufficient for deployment — no sidecar configuration required.
275
 
276
+ Every file below is the artifact a validation session in the tables above measured. Files are organized into per-target folders and follow the naming convention `yolo11{size}-seg-{precision}[-smart|-logical][-end2end]{extension}`.
277
+
278
+ Entries marked **2026 Q2** belong to rows carried from the 2026 Q2 edition: they download from the repository's `2026Q2` tag, the weights those rows were measured on. The same filename on `main` holds the current edition's artifact.
279
+
280
+ <details open>
281
+ <summary><strong>ONNX FP32</strong> — Any platform with ONNX Runtime.</summary>
282
+
283
+ | Size | File | Variant | Download |
284
+ |------|------|---------|----------|
285
+ | Nano | `onnx/yolo11n-seg-fp16.onnx` | FP16 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/onnx/yolo11n-seg-fp16.onnx) |
286
+ | Nano | `onnx/yolo11n-seg-fp16.onnx` | FP16 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/onnx/yolo11n-seg-fp16.onnx) |
287
+ | Nano | `onnx/yolo11n-seg-fp32.onnx` | FP32 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/onnx/yolo11n-seg-fp32.onnx) |
288
+ | Nano | `onnx/yolo11n-seg-fp32.onnx` | FP32 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/onnx/yolo11n-seg-fp32.onnx) |
289
+ | Small | `onnx/yolo11s-seg-fp16.onnx` | FP16 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/onnx/yolo11s-seg-fp16.onnx) |
290
+ | Small | `onnx/yolo11s-seg-fp16.onnx` | FP16 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/onnx/yolo11s-seg-fp16.onnx) |
291
+ | Small | `onnx/yolo11s-seg-fp32.onnx` | FP32 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/onnx/yolo11s-seg-fp32.onnx) |
292
+ | Small | `onnx/yolo11s-seg-fp32.onnx` | FP32 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/onnx/yolo11s-seg-fp32.onnx) |
293
+ | Medium | `onnx/yolo11m-seg-fp16.onnx` | FP16 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/onnx/yolo11m-seg-fp16.onnx) |
294
+ | Medium | `onnx/yolo11m-seg-fp16.onnx` | FP16 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/onnx/yolo11m-seg-fp16.onnx) |
295
+ | Medium | `onnx/yolo11m-seg-fp32.onnx` | FP32 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/onnx/yolo11m-seg-fp32.onnx) |
296
+ | Medium | `onnx/yolo11m-seg-fp32.onnx` | FP32 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/onnx/yolo11m-seg-fp32.onnx) |
297
+
298
+ </details>
299
+
300
+ <details>
301
+ <summary><strong>TFLite INT8</strong> — CPU or NPU via runtime delegate (NXP i.MX 8M Plus VX Delegate).</summary>
302
+
303
+ | Size | File | Variant | Download |
304
+ |------|------|---------|----------|
305
+ | Nano | `tflite/yolo11n-seg-int8-smart.tflite` | Smart · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/tflite/yolo11n-seg-int8-smart.tflite) |
306
+ | Nano | `tflite/yolo11n-seg-int8-smart.tflite` | Smart · INT8 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/tflite/yolo11n-seg-int8-smart.tflite) |
307
+ | Nano | `tflite/yolo11n-seg-int8.tflite` | Logical · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/tflite/yolo11n-seg-int8.tflite) |
308
+ | Nano | `tflite/yolo11n-seg-int8.tflite` | Logical · INT8 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/tflite/yolo11n-seg-int8.tflite) |
309
+ | Small | `tflite/yolo11s-seg-int8-smart.tflite` | Smart · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/tflite/yolo11s-seg-int8-smart.tflite) |
310
+ | Small | `tflite/yolo11s-seg-int8-smart.tflite` | Smart · INT8 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/tflite/yolo11s-seg-int8-smart.tflite) |
311
+ | Small | `tflite/yolo11s-seg-int8.tflite` | Logical · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/tflite/yolo11s-seg-int8.tflite) |
312
+ | Medium | `tflite/yolo11m-seg-int8-smart.tflite` | Smart · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/tflite/yolo11m-seg-int8-smart.tflite) |
313
+ | Medium | `tflite/yolo11m-seg-int8-smart.tflite` | Smart · INT8 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/tflite/yolo11m-seg-int8-smart.tflite) |
314
+ | Medium | `tflite/yolo11m-seg-int8.tflite` | Logical · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/tflite/yolo11m-seg-int8.tflite) |
315
+
316
+ </details>
317
+
318
+ <details>
319
+ <summary><strong>NXP i.MX 95 (eIQ Neutron)</strong> — eIQ Neutron NPU optimized.</summary>
320
+
321
+ | Size | File | Variant | Download |
322
+ |------|------|---------|----------|
323
+ | Nano | `imx95/yolo11n-seg-int8-smart.imx95.tflite` | Smart · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/imx95/yolo11n-seg-int8-smart.imx95.tflite) |
324
+ | Nano | `imx95/yolo11n-seg-int8.imx95.tflite` | Logical · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/imx95/yolo11n-seg-int8.imx95.tflite) |
325
+ | Small | `imx95/yolo11s-seg-int8-smart.imx95.tflite` | Smart · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/imx95/yolo11s-seg-int8-smart.imx95.tflite) |
326
+ | Small | `imx95/yolo11s-seg-int8.imx95.tflite` | Logical · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/imx95/yolo11s-seg-int8.imx95.tflite) |
327
+
328
+ </details>
329
+
330
+ <details>
331
+ <summary><strong>NXP Ara240</strong> — Ara240 DNPU compiled model.</summary>
332
+
333
+ | Size | File | Variant | Download |
334
+ |------|------|---------|----------|
335
+ | Nano | `ara240/yolo11n-seg-int16.dvm` | Smart · INT16 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/ara240/yolo11n-seg-int16.dvm) |
336
+ | Small | `ara240/yolo11s-seg-int16.dvm` | Smart · INT16 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/ara240/yolo11s-seg-int16.dvm) |
337
+ | Medium | `ara240/yolo11m-seg-int16.dvm` | Smart · INT16 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/ara240/yolo11m-seg-int16.dvm) |
338
+
339
+ </details>
340
+
341
+ <details>
342
+ <summary><strong>RPi5 + Hailo-8L (13 TOPS)</strong> — Hailo-8L 13 TOPS accelerator.</summary>
343
+
344
+ | Size | File | Variant | Download |
345
+ |------|------|---------|----------|
346
+ | Nano | `hailo/yolo11n-seg-int8.hailo8l.hef` | INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/hailo/yolo11n-seg-int8.hailo8l.hef) |
347
+ | Small | `hailo/yolo11s-seg-int8.hailo8l.hef` | INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/hailo/yolo11s-seg-int8.hailo8l.hef) |
348
+ | Medium | `hailo/yolo11m-seg-int8.hailo8l.hef` | INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/hailo/yolo11m-seg-int8.hailo8l.hef) |
349
+
350
+ </details>
351
+
352
+ <details>
353
+ <summary><strong>NVIDIA Jetson (TensorRT)</strong> — Jetson FP16 and INT8 engines.</summary>
354
+
355
+ | Size | File | Variant | Download |
356
+ |------|------|---------|----------|
357
+ | Nano | `jetson/yolo11n-seg-fp16.engine` | FP16 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/jetson/yolo11n-seg-fp16.engine) |
358
+ | Nano | `jetson/yolo11n-seg-int8.engine` | INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/jetson/yolo11n-seg-int8.engine) |
359
+ | Small | `jetson/yolo11s-seg-fp16.engine` | FP16 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/jetson/yolo11s-seg-fp16.engine) |
360
+ | Small | `jetson/yolo11s-seg-int8.engine` | INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/jetson/yolo11s-seg-int8.engine) |
361
+ | Medium | `jetson/yolo11m-seg-fp16.engine` | FP16 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/jetson/yolo11m-seg-fp16.engine) |
362
+ | Medium | `jetson/yolo11m-seg-int8.engine` | INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/jetson/yolo11m-seg-int8.engine) |
363
+
364
+ </details>
365
+
366
+ <details>
367
+ <summary><strong>Qualcomm Snapdragon 8 Elite Gen 5 (Hexagon v81)</strong> — Offline-compiled Hexagon context binary, run through the ONNX Runtime QNN execution provider. Loads only on the Snapdragon 8 Elite Gen 5 (Hexagon v81).</summary>
368
+
369
+ | Size | File | Variant | Download |
370
+ |------|------|---------|----------|
371
+ | Nano | `qnn/yolo11n-seg-int16-logical.htp-v81.qnn.onnx` | Logical · INT16 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/qnn/yolo11n-seg-int16-logical.htp-v81.qnn.onnx) |
372
+ | Nano | `qnn/yolo11n-seg-int16-smart.htp-v81.qnn.onnx` | Smart · INT16 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/qnn/yolo11n-seg-int16-smart.htp-v81.qnn.onnx) |
373
+ | Nano | `qnn/yolo11n-seg-int8-logical.htp-v81.qnn.onnx` | Logical · INT8 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/qnn/yolo11n-seg-int8-logical.htp-v81.qnn.onnx) |
374
+ | Nano | `qnn/yolo11n-seg-int8-smart.htp-v81.qnn.onnx` | Smart · INT8 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/qnn/yolo11n-seg-int8-smart.htp-v81.qnn.onnx) |
375
+ | Small | `qnn/yolo11s-seg-int16-logical.htp-v81.qnn.onnx` | Logical · INT16 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/qnn/yolo11s-seg-int16-logical.htp-v81.qnn.onnx) |
376
+ | Small | `qnn/yolo11s-seg-int16-smart.htp-v81.qnn.onnx` | Smart · INT16 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/qnn/yolo11s-seg-int16-smart.htp-v81.qnn.onnx) |
377
+ | Small | `qnn/yolo11s-seg-int8-logical.htp-v81.qnn.onnx` | Logical · INT8 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/qnn/yolo11s-seg-int8-logical.htp-v81.qnn.onnx) |
378
+ | Small | `qnn/yolo11s-seg-int8-smart.htp-v81.qnn.onnx` | Smart · INT8 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/qnn/yolo11s-seg-int8-smart.htp-v81.qnn.onnx) |
379
+ | Medium | `qnn/yolo11m-seg-int16-logical.htp-v81.qnn.onnx` | Logical · INT16 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/qnn/yolo11m-seg-int16-logical.htp-v81.qnn.onnx) |
380
+ | Medium | `qnn/yolo11m-seg-int16-smart.htp-v81.qnn.onnx` | Smart · INT16 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/qnn/yolo11m-seg-int16-smart.htp-v81.qnn.onnx) |
381
+ | Medium | `qnn/yolo11m-seg-int8-logical.htp-v81.qnn.onnx` | Logical · INT8 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/qnn/yolo11m-seg-int8-logical.htp-v81.qnn.onnx) |
382
+ | Medium | `qnn/yolo11m-seg-int8-smart.htp-v81.qnn.onnx` | Smart · INT8 · 2026 Q2 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/2026Q2/qnn/yolo11m-seg-int8-smart.htp-v81.qnn.onnx) |
383
+
384
+ </details>
385
+
386
+ <details>
387
+ <summary><strong>Qualcomm Dragonwing IQ-9075 (Hexagon v73)</strong> — Offline-compiled Hexagon context binary, run through the ONNX Runtime QNN execution provider. Loads only on the Dragonwing IQ-9075 (Hexagon v73).</summary>
388
+
389
+ | Size | File | Variant | Download |
390
+ |------|------|---------|----------|
391
+ | Nano | `qnn/yolo11n-seg-int16-smart.iq9075.qnn.onnx` | Smart · INT16 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/qnn/yolo11n-seg-int16-smart.iq9075.qnn.onnx) |
392
+ | Nano | `qnn/yolo11n-seg-int8-logical.iq9075.qnn.onnx` | Logical · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/qnn/yolo11n-seg-int8-logical.iq9075.qnn.onnx) |
393
+ | Nano | `qnn/yolo11n-seg-int8-smart.iq9075.qnn.onnx` | Smart · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/qnn/yolo11n-seg-int8-smart.iq9075.qnn.onnx) |
394
+ | Small | `qnn/yolo11s-seg-int16-smart.iq9075.qnn.onnx` | Smart · INT16 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/qnn/yolo11s-seg-int16-smart.iq9075.qnn.onnx) |
395
+ | Small | `qnn/yolo11s-seg-int8-logical.iq9075.qnn.onnx` | Logical · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/qnn/yolo11s-seg-int8-logical.iq9075.qnn.onnx) |
396
+ | Small | `qnn/yolo11s-seg-int8-smart.iq9075.qnn.onnx` | Smart · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/qnn/yolo11s-seg-int8-smart.iq9075.qnn.onnx) |
397
+ | Medium | `qnn/yolo11m-seg-int16-smart.iq9075.qnn.onnx` | Smart · INT16 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/qnn/yolo11m-seg-int16-smart.iq9075.qnn.onnx) |
398
+ | Medium | `qnn/yolo11m-seg-int8-logical.iq9075.qnn.onnx` | Logical · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/qnn/yolo11m-seg-int8-logical.iq9075.qnn.onnx) |
399
+ | Medium | `qnn/yolo11m-seg-int8-smart.iq9075.qnn.onnx` | Smart · INT8 | [Download](https://huggingface.co/EdgeFirst/yolo11-seg/resolve/main/qnn/yolo11m-seg-int8-smart.iq9075.qnn.onnx) |
400
+
401
+ </details>
402
 
 
 
 
 
 
 
 
 
 
 
 
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