yolox-p150 on Tenstorrent p150

Derived from AutowareFoundation/tensorrt_yolox (tag v1.0), the YOLOX-s+ seg16 network that TIER IV trained for Autoware's autoware_tensorrt_yolox camera detector, with unmodified weights. YOLOX: Ge et al., arXiv:2107.08430. See the original model card for training details.

YOLOX-s+ seg16, a 15.5M-parameter single-stage camera detector (ELAN CSPDarknet backbone, PAFPN neck, anchor-free decoupled head on strides 8, 16 and 32) with a 16-class semantic-segmentation decoder, run on a 960x960 letterboxed input (121 GFLOPs per image). One camera image of any size goes in. Out come 2D boxes for 8 classes (UNKNOWN, CAR, TRUCK, BUS, BICYCLE, MOTORBIKE, PEDESTRIAN, ANIMAL), the Autoware ROI records and a 16-class semantic mask, with the pre- and post-processing of the autoware_tensorrt_yolox node (letterbox, decode, NMS, class remap, ROI overlay on the mask).

Runs on Tenstorrent p150 (mesh P150).

Packaged and published with tt-model-manager 0.1.0 (manifest schema 5.1).

At a glance

Hardware	p150
License	apache-2.0

Intended use

Camera 2D object detection and semantic segmentation for Autoware-style perception: research, prototyping and offline processing of recorded driving images, with the outputs of one autoware_tensorrt_yolox node per camera.

Prerequisites

• The Tenstorrent CLI, tt: uv tool install tenstorrent (or use tt-model alone, see the Quickstart)
• Docker
• Tenstorrent hardware: p150

Quickstart

tt serve changh95/yolox-p150

tt serve downloads the Docker image and the AutowareFoundation/tensorrt_yolox at 424d6b9cb94c939ae9ce6a22380e46c13f76b1da weights (into your HF cache; they are not in the image), then starts the model's own HTTP server on port 20000 (or the next free port, if that one is busy). The first start compiles kernels for your device, which takes several minutes; the server is ready when it logs Application startup complete.

Without tt-cli: tt-model serve changh95/yolox-p150.

Send the sample image shipped in this repo (Autoware's own test image) to /predict, on the port tt serve reported:

curl -sLO https://huggingface.co/changh95/yolox-p150/resolve/main/code/tt_yolox/samples/test_image.jpg
printf '{"images": [{"camera": "CAM_FRONT", "data": "%s"}]}' "$(base64 -w0 test_image.jpg)" > req.json
curl -s localhost:20000/predict -H 'Content-Type: application/json' -d @req.json

Capabilities

This package exposes the model's own HTTP server on port 20000, not an OpenAI-compatible chat API — its request and response shapes are the model's own. See the author's notes for the payload it expects.

POST /predict takes one camera image as JSON and returns its detections, ROIs and mask. GET /health and GET /info report the server and device state; GET /v1/models is a stub, not a chat API.

request field	meaning
images	exactly one entry, {"camera": "<name>", "data": "<base64 PNG or JPEG>"}; any image size; decoded to BGR8 as the Autoware node receives it
params (optional)	score_threshold (default 0.35), nms_threshold (0.7), is_roi_overlap_segmentation (true), overlap_roi_score_threshold (0.3), is_publish_color_mask (false)
output_format (optional)	json (default; the mask as a base64 PNG) or npz (the mask and the box arrays as a lossless npz)

Response for the sample image (two of the nine detections and one ROI shown, the mask data elided):

{"model": "yolox-p150", "frame_id": "camera", "num_detections": 9,
 "detections": [
   {"label": "CAR", "label_id": 1, "score": 0.9356, "box_xyxy": [344.0, 376.0, 398.0, 410.0]},
   {"label": "TRUCK", "label_id": 2, "score": 0.9307, "box_xyxy": [556.0, 308.0, 832.0, 465.0]}],
 "semseg": {"format": "png", "key": "mask", "dtype": "uint8", "shape": [620, 960], "data": "<base64 PNG>"},
 "rois": [{"x_offset": 344, "y_offset": 376, "width": 54, "height": 34, "label": "CAR", "label_id": 1, "autoware_label": 1, "score": 0.935619}],
 "meta": {"variant": "clip6", "image_hw": [827, 1280], "mask_hw": [620, 960], "scale": 0.75},
 "timing_ms": {"preprocess": 9.1, "device": 8.9, "postprocess": 1.5, "total": 48.5, "decode": 19.5, "model_call": 19.5}}

• box_xyxy is in original-image pixels: the integer Autoware ROI, also returned in rois with the Autoware ObjectClassification id autoware_label. Detections are sorted by score.
• semseg is the Autoware mask: one uint8 class id per pixel (the 16 classes of semseg_color_map.csv in the weights repo), at network resolution, cropped to the image area (620x960 for a 1280x827 image).
• A Python API with the same outputs (tt_yolox.YOLOX) is described in code/PYTHON.md; it needs a tt-metal build with the two patches in this repo's patches/ directory.

Example output

Boxes and segmentation computed by this package on 60 front-camera images of nuScenes scene-1094 (Singapore, night after rain, about 12 Hz), also as MP4. Non-commercial use only: rendered from the nuScenes dataset, © Motional AD Inc., CC BY-NC-SA 4.0 and the nuScenes Terms of Use; Motional does not endorse this work. Attributions for every file in media/: media/ATTRIBUTION.md.

Expected performance

Agreement with the fp32 CPU reference of the same network (same weights, same pre- and post-processing):

eval	this package	fp32 CPU reference	images
Detections matched one-to-one (same class, IoU ≥ 0.5), sample image	9 of 9, max score difference 0.0024	9	1
Detections matched one-to-one, public driving images	3,370 of 3,394 (99.3 %)	3,394	265
Detections with score ≥ 0.40 that change class, public driving images	1	–	265
Detection-head output PCC, sample image / minimum over 24 public images	0.99956 / 0.99885	–	1 / 24
Semantic-mask pixels equal to the reference, sample image	99.85 %	–	1
Semantic-mask pixels equal to the reference, public driving images: mean / minimum	99.76 % / 98.40 %	–	265
2D recall against projected 3D ground-truth boxes (IoU ≥ 0.3), cars	448 / 462 (97.0 %)	448 / 462 (97.0 %)	238
2D recall against projected 3D ground-truth boxes (IoU ≥ 0.3), pedestrians	421 / 571 (73.7 %)	421 / 571 (73.7 %)	238

Public driving images: all cameras of PandaSet sequences 005, 019 (day) and 065 (night) and of nuScenes v1.0-mini scenes (day and night). The recall rows use the 238 of them that have 3D labels; they check the detector against ground truth, not a 2D benchmark. The weights repo publishes no dataset-level accuracy to compare with.

speed (batch 1, warm)	input	median	throughput
Device time per image, back-to-back trace replays	960x960 network input	6.49 ms	154 images/s
Python model() call (decode, letterbox, device, post-processing)	1280x827 JPEG (sample image)	30.1 ms (p99 31.3)	33 images/s
Python model() call	1920x1080 JPEG (PandaSet)	30.2 ms (p99 32.4)	33 images/s
Served POST /predict, server timing_ms.total	1280x827 JPEG (sample image)	48.9 ms	–
Served POST /predict, client round trip on loopback	1280x827 JPEG (sample image)	55.0 ms	–

Measured with this package's code on one p150 in an 8-vCPU AMD EPYC-Rome host: code/scripts/bench.py (100 warm iterations per stage; device time over 5 rounds of 50 back-to-back replays) and 50 served requests of the sample image. The model() and served times are host-bound: image decode and letterbox take about 18 ms of the call on the sample image and 19.5 ms on the PandaSet image.

Limitations

• Do not use it to drive or control a vehicle or as any safety function: it is not a certified Autoware component and has no functional-safety qualification.
• Do not use its outputs as a YOLOX accuracy benchmark: the weights are TIER IV's T4 fine-tune, whose training data is not published, and accuracy on other cameras and domains is not characterized.
• Not a ROS 2 node: it serves HTTP and Python only. It runs the seg16 model of the weights repo; the detection-only, tiny and traffic-light models are not included.
• Batch 1, one image per request; requests are served one at a time. The network input is fixed at 960x960.
• One p150 only. The image runs device dispatch on the chip's Ethernet cores, so multi-chip meshes are not supported.
• Numerics follow Autoware's deployed engine in saturation (every activation clipped at 6), not in int8 quantization; outputs were compared with the fp32 CPU reference, not with TensorRT.
• bf16 activations: scores and boxes differ slightly from the fp32 reference. An object near the 0.35 score threshold or a near-duplicate pair near the 0.7 NMS IoU can appear or disappear, and an object whose two best classes score almost equally can change class (1 object above score 0.40 in 265 public images).
• On public driving images the weights themselves (on CPU and on p150 alike) label San Francisco buses as TRUCK, almost never predict sidewalk (it comes out as road), predict crosswalk_others on road texture and give construction barriers no box.

Licensing

• Weights: AutowareFoundation/tensorrt_yolox at tag v1.0 (commit 424d6b9cb94c939ae9ce6a22380e46c13f76b1da), Apache-2.0 per its model card. Downloaded by tt serve at that commit, not redistributed here. The upstream card does not document the training data of its T4 fine-tunes.
• Pre- and post-processing ported from autoware_universe perception/autoware_tensorrt_yolox (Apache-2.0).
• Port and serving code (code/) and the tt-metal patches in patches/: Apache-2.0.
• Sample image code/tt_yolox/samples/test_image.jpg: the Autoware package's own test image (Apache-2.0).
• Demo media in media/ are renders, not part of the package. The demo video and media/*_NC.jpg come from nuScenes: non-commercial use only, CC BY-NC-SA 4.0, © Motional AD Inc. The PandaSet renders: CC BY 4.0 and the PandaSet Dataset Terms (Scale AI and Hesai). Neither dataset owner endorses this work. Full attributions: media/ATTRIBUTION.md.

Related packages

• The Autoware collection: the other Autoware perception and planning models packaged for the p150.

Feedback

Questions or problems with this package: open a discussion at https://huggingface.co/changh95/yolox-p150/discussions — that is what reaches its author. A problem with the tt tooling itself: tt report issue (collects your environment and opens a prefilled issue against tenstorrent/tt-cli). Product feedback: support@tenstorrent.com.

Provenance

The exact sources the image was built from — code/ in this repo is byte-identical to the model code inside the image:

component	built from
tt-metal	44d66500520fda9f2c7060c0f6b41ec48f7ab37e (dirty tree — the image includes uncommitted changes)
code/ digest	42fe41168085a734 (sha256, first 16 hex digits)
built	2026-10-16T16:37:27+00:00 by tt-model 0.1.0