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+# YOLOv10
+
+This family builds YOLOv10 detectors (`jameslahm/yolov10n`, `yolov10s`,
+`yolov10x`) for `image_to_boxes` as one end-to-end TensorRT engine: 640 x 640
+input, batch 1, fp16 or fp32 (the manifests use fp16). The one-to-one head emits
+one box per object, so decode and top-k run in the engine and no non-max
+suppression is needed. The family has no quantized build.
+
+## FP8 quantization study (not shipped)
+
+FP8 self-quantization of the convolutions was built and measured on TensorRT
+11.4 (and 11.1) for `jameslahm/yolov10x`. It is not shipped: the plan is 43%
+smaller, but latency stays at the fp16 level (0.98x to 1.02x at batch 1) and COCO
+AP drops by 0.3 point. On yolov10n and yolov10s FP8 is slower than fp16. This
+section records the recipe, the method and the numbers, so that the study does
+not have to be repeated. The FP8 code was not merged; this family has no FP8
+build.
+
+### Summary and decision
+
+| Item | fp16 (manifest) | FP8 (fp16 engine) | FP8 vs fp16 |
+|---|---|---|---|
+| yolov10x latency, batch 1, B200 / B300 (TensorRT 11.4) | 1.406 / 1.323 ms | 1.377 / 1.319 ms | 1.02x / 1.00x |
+| yolov10x latency, batch 8, B200 / B300 (TensorRT 11.4) | 4.059 / 3.815 ms | 4.077 / 3.861 ms | 1.00x / 0.99x |
+| yolov10x plan size | 61.9 MiB | 35.5 MiB | -43% |
+| yolov10x COCO AP, B200 / B300 (PyTorch fp32 0.5731) | 0.5730 / 0.5734 | 0.5697 / 0.5701 | -0.3 point |
+| yolov10n / yolov10s latency, batch 1, B200 | - | - | 0.93x / 0.97x |
+| Non-finite outputs | 0 | 0 | none |
+
+Decision: do not ship FP8 for YOLOv10.
+
+- Speed: the FP8 convolutions are about 11% faster than the fp16 ones, but the
+  bias, SiLU and the Quantize of the next convolution are not fused into the FP8
+  convolution. They run as separate pointwise kernels (62 in the fp16 plan, 104
+  in the FP8 plan) and take the gain back.
+- The depthwise convolutions (about a sixth of the time) have no FP8 kernel and
+  stay fp16.
+- Smaller models: yolov10n and yolov10s are slower in FP8 and lose 0.35 to 0.9
+  AP point.
+- The only gain is plan size (27 MiB less for yolov10x).
+
+### Recipe that was measured
+
+No FP8 or NVFP4 checkpoint of any YOLO detector exists on the Hub, and no
+official quantization config exists for one. The recipe follows the convolution
+guidance used for the `stable_diffusion` FP8 build: quantize the convolutions,
+keep the input convolution. Each quantized convolution is built as
+`input -> Quantize -> Dequantize(fp16) -> Convolution(+bias)`, with the kernel
+`FP8 constant -> Dequantize(fp16, axis 0)`.
+
+| Choice | Decision |
+|---|---|
+| Quantized (108 of 173 convolutions in yolov10x) | every dense convolution whose input and output channels are multiples of 16: Conv blocks, C2f, bottleneck 3x3, CIB 1x1, SCDown `cv1`, SPPF, PSA `cv1`, `cv2`, `attn.qkv`, `attn.proj`, `ffn`, head dense steps and the final box and class convolutions |
+| fp16 | stem `model.0` (3 input channels), all 64 depthwise convolutions (CIB, SCDown `cv2`, PSA `attn.pe`, class branch), attention matmuls and softmax, DFL decode and top-k |
+| Weight scale | per output channel, amax / 448 of the batch-norm-folded kernel, at build time |
+| Activation scale | static, per tensor; ModelOpt 0.46.1 `FP8_DEFAULT_CFG` (max calibration) on the family's PyTorch reference, 256 letterboxed COCO 2017 validation images |
+| Outlier rule | a scale above 20x the median of its kind (1x1 or spatial) stays fp16; measured: no outlier (largest input amax 164, median 14.5) |
+| Dequantize type | fp16; no non-finite value, fp32 Dequantize was not needed |
+
+### Method
+
+- GPUs: B200 and B300, one GPU per job; TensorRT 11.4.0.106 (target) and
+  11.1.0.106.
+- Quality: COCO 2017 validation images 300 to 799 of `detection-datasets/coco`
+  (500 held out; 0 to 255 are the calibration set), letterboxed like the
+  runtime. AP is `pycocotools` COCO AP (IoU 0.50 to 0.95) of the 300 detections
+  per image, comparable only inside these tables. Agreement: detections at score
+  0.25 matched to the PyTorch fp32 detections (same class, IoU at least 0.5).
+- Non-finite scan: the 500 images and 20 stress inputs (noise, constants).
+- Speed: CUDA graph replay, median of 9 interleaved rounds. Batch 8 is a scratch
+  build (the family builds batch 1 only).
+
+### Results
+
+Speed and size, yolov10x (x is fp16 time over FP8 time):
+
+| GPU | TensorRT | fp32 ms | fp16 ms | FP8 ms | x batch 1 | x batch 8 | plan MiB fp16 to FP8 |
+|---|---|---|---|---|---|---|---|
+| B200 | 11.4 | 2.021 | 1.406 | 1.377 | 1.02 | 1.00 | 61.9 to 35.5 |
+| B300 | 11.4 | 1.927 | 1.323 | 1.319 | 1.00 | 0.99 | 62.0 to 35.5 |
+| B200 | 11.1 | 2.015 | 1.412 | 1.385 | 1.02 | 1.03 | 61.6 to 35.1 |
+| B300 | 11.1 | 1.919 | 1.305 | 1.329 | 0.98 | 1.02 | 61.7 to 35.2 |
+
+Quality, yolov10x, B200 (B300 in brackets where it differs; TensorRT 11.1 gives
+the same numbers as 11.4):
+
+| Engine | AP | AP50 | prec / rec vs PyTorch | box IoU | score error |
+|---|---|---|---|---|---|
+| PyTorch fp32 | 0.5731 | 0.7334 | - | - | - |
+| fp16 engine | 0.5730 (0.5734) | 0.7337 | 0.999 / 0.999 (0.998 / 0.998) | 0.995 | 0.0010 |
+| FP8 engine | 0.5697 (0.5701) | 0.7302 (0.7294) | 0.977 / 0.959 (0.977 / 0.958) | 0.983 | 0.024 |
+
+Kernel time per group, yolov10x, batch 1, B200, TensorRT 11.4 (layer profiler,
+ms (count)):
+
+| Kernel group | fp16 | FP8 |
+|---|---|---|
+| dense convolutions | 1.415 (111) | 1.212 FP8 (108) + 0.049 fp16 (3) |
+| depthwise convolutions | 0.531 (64) | 0.529 (64) |
+| bias + SiLU (+ Quantize) pointwise | 0.484 (62) | 0.800 (104) |
+| layout transposes | 0.343 (47) | 0.386 (54) |
+| other | 0.326 | 0.175 |
+| sum | 3.09 | 3.15 |
+
+B300 shows the same picture (sum 3.02 fp16, 3.12 FP8).
+
+Variants (TensorRT 11.4, B200 speed vs fp16; one separate job, so the AP of the
+first row differs slightly from the table above):
+
+| Variant | x batch 1 / batch 8 | plan MiB | AP B200 / B300 |
+|---|---|---|---|
+| recipe above | 1.02 / 1.00 | 35.5 | 0.5707 / 0.5720 |
+| per-tensor weight scale (ModelOpt default) | 1.01 / - | 35.4 | 0.5681 / 0.5681 |
+| without the six last head convolutions | 1.00 / - | 35.4 | 0.5699 / 0.5699 |
+| 3x3 convolutions only (33) | 1.01 / 1.02 | 55.4 | 0.6014 (100 images, fp16 0.5953) |
+| 1x1 convolutions only (75) | 0.96 / 0.95 | 42.4 | 0.5956 (100 images, fp16 0.5953) |
+| builder optimization level 3 / 5 | 1.00 / 1.01 | 35.2 | - |
+| depthwise convolutions in FP8 | build fails on 11.4 ("no tactics") | | |
+| yolov10n | 0.93 (B300 0.92) / 0.88 | 6.2 to 4.5 | 0.4128 / 0.4102 (fp16 0.4190) |
+| yolov10s | 0.97 (B300 0.95) / 0.95 (0.94) | 15.7 to 9.5 | 0.4865 / 0.4882 (fp16 0.4917) |
+
+### NVFP4 (not tried)
+
+TensorRT does not support NVFP4 on convolutions (4-D tensors), and the only
+matmuls of the model (the PSA attention) are a negligible share of the time.
+
+### What was not tried
+
+| Item | Note |
+|---|---|
+| FP8 attention matmuls, percentile or entropy calibration | not measured |
+| A SiLU form that TensorRT might fuse into the FP8 convolution epilogue | not measured |
+| Full 5000-image COCO validation, H100, native runtime with an FP8 bundle | not measured |
+
+### When to revisit
+
+- A TensorRT release that fuses bias, SiLU and the next Quantize into the FP8
+  convolution epilogue (as it does for most fp16 convolutions): the saved 0.15
+  to 0.2 ms of convolution time would then show up as a speedup.
+- A TensorRT FP8 kernel for depthwise convolutions.
+
+Batch 8 gave the same ratio as batch 1, so larger batches alone do not change
+the result.