yoloe-26n-seg

AXERA-TECH/yoloe-26n-seg

yoloe-26n-seg is a machine learning model from AXERA-TECH. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

This version of YOLOE-26n-Seg has been converted to run on the Axera NPU using U16 mixed-precision quantization.

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Updated Sep 3, 2026

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License: mit
Access: Public
Created: Sep 2, 2026
Updated: Sep 3, 2026
SHA: 92b80871

Model card

From the Hugging Face model README

YOLOE-26n-Seg

This version of YOLOE-26n-Seg has been converted to run on the Axera NPU using U16 mixed-precision quantization.

Converted with Pulsar2 version: 7.0

Model Splitting

Original model repository:

git clone https://huggingface.co/openvision/yoloe26-n-seg

Model	Description	Input	Output
`models/0_yoloe-26n-seg.pt`	Original PyTorch model	RGB image	Detection boxes, classes, and instance masks
`models/4_m1_cut_yoloe-26n-seg_640_pulsar2.onnx`	ONNX graph to be compiled by Pulsar2	`images`, FP32 NCHW `[1,3,640,640]`	`bbox`, `class_scores`, `mask_coeff`, `proto`
`models/4_m1_cut_yoloe-26n-seg_640_post.onnx`	Host post-processing graph	`bbox`, `class_scores`, `mask_coeff`	`output0`
`models/yoloe_26n_seg_m1.axmodel`	AX650 deployment model	RGB NHWC U8 `[1,640,640,3]`	`bbox`, `class_scores`, `mask_coeff`, `proto`

The interfaces of the split models are as follows:

Iutput of AXModel:

  images        [1, 3, 640, 640]    FP32

Outputs of AXModel:
  bbox          [1, 4, 8400]        FP32
  class_scores  [1, 80, 8400]       FP32
  mask_coeff    [1, 32, 8400]       FP32
  proto         [1, 32, 160, 160]   FP32

Output of the Host post-processing graph:
  output0       [1, 300, 38]        FP32

The 38 attributes of each detection in output0 are arranged as follows:

[0:4]    xyxy detection box
[4]      score
[5]      class_id
[6:38]   32-dimensional mask coefficient

The proto tensor and the mask coefficients in output0 are used together to decode instance masks. The Pulsar2 input graph, Host post-processing graph, and axmodel must come from the same model version and must not be mixed with other versions.

Scripts

Script	Runtime	Description
`src/tool/export_onnx.py`	Development machine	Sets the COCO 80-class text features for YOLOE and exports a fixed 640×640 FP32 ONNX model from the PT model.
`src/tool/modify_onnx.py`	Development machine	Replaces `Mod` operators in the ONNX model and optionally downgrades the opset to 18.
`src/tool/e2e_cut.py`	Development machine	Extracts the ONNX graph submitted to Pulsar2 and the Host post-processing graph from the full ONNX model.
`src/infer/e2e_infer_onnx.py`	Development machine	Runs the PT or FP32 ONNX model with Ultralytics or ONNX Runtime, supporting single-image inference, dataset inference, and performance measurements.
`src/infer/e2e_infer_axmodel.py`	AX650	Runs the `axmodel` with axengine and executes the Host post-processing graph with ONNX Runtime.
`src/tool/evaluate_npz.py`	Development machine	Compares single-image ONNX and `axmodel` NPZ outputs using cosine similarity, MSE, and maximum absolute error.
`src/tool/evaluate_yoloe.py`	Development machine	Converts inference results to COCO format, evaluates bbox/segm mAP, and compares two `metrics.json` files.

The compilation configuration is pulsar2_u16_ax650.json, and the calibration dataset is datasets/coco2017val_64_calibration_dataset.tar.

The datasets/ directory contains only example images and calibration data. For official accuracy validation, download the complete COCO 2017 validation dataset.

Results

YOLOE-26n-Seg `axmodel` Evaluation Results

bbox

========== COCO bbox evaluation ==========
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.264
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.394
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.280
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.118
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.296
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.391
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.285
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.465
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.490
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.276
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.535
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.682

segm

========== COCO segm evaluation ==========
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.222
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.372
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.229
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.076
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.239
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.354
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.244
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.383
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.401
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.206
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.450
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.575

Images: 5000
Predictions: 790687
bbox AP@[0.5:0.95]: 0.2644
segm AP@[0.5:0.95]: 0.2219

YOLOE-26n-Seg `onnx` Evaluation Results

========== COCO bbox evaluation ==========
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.280
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.415
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.296
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.123
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.306
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.412
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.293
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.480
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.506
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.303
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.548
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.702

========== COCO segm evaluation ==========
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.232
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.391
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.239
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.083
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.250
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.367
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.250
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.393
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.412
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.221
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.585

Images: 5000
Predictions: 767463
bbox AP@[0.5:0.95]: 0.2796
segm AP@[0.5:0.95]: 0.2321

YOLOE-26n-Seg `axmodel` Inference Performance

Model	CMM (MB)	NPU Latency (ms, Avg)	FPS	Test Conditions
`yoloe_26n_seg_m1.axmodel`	48.61	32.099	31.15	AX650, input 640×640, batch=1, warmup=10, repeat=100

cmm size: 48,611,218 bytes, approximately 48.61 MB in decimal units or 46.36 MiB;
Average NPU inference latency: 32.099 ms;
FPS calculated as 1000 / avg_ms: 1000 / 32.099 = 31.15 FPS;
The ax_run_model result includes only axmodel inference. It excludes image reading, preprocessing, Host post-processing, NMS, mask decoding, encoding, visualization, and file I/O.

FP32 and `axmodel` Accuracy Comparison

Evaluation dataset:
Image count:

metric                  | FP32       | axmodel    | drop
------------------------|------------|------------|-----
bbox AP@[0.5:0.95]     | 0.279609   | 0.264382   | 0.015228
bbox [email protected]            | 0.414795   | 0.394218   | 0.020577
bbox [email protected]           | 0.295700   | 0.279766   | 0.015934
segm AP@[0.5:0.95]     | 0.232135   | 0.221909   | 0.010226
segm [email protected]            | 0.390976   | 0.372190   | 0.018786
segm [email protected]           | 0.238784   | 0.228958   | 0.009826

Basic Workflow

# 1. Clone the model repository
git clone https://huggingface.co/AXERA-TECH/yoloe-26n-seg
cd yoloe-26n-seg

# 2. Prepare the full FP32 ONNX model
# Update the model paths in src/tool/export_onnx.py for the current directory layout.
python src/tool/export_onnx.py

# 3. Optionally replace Mod and downgrade the opset to 18
python src/tool/modify_onnx.py \
  --input <full_FP32_ONNX> \
  --output <modified_ONNX> \
  --replace-mod \
  --downgrade-opset 18 \
  --check

# 4. Split the ONNX model
python src/tool/e2e_cut.py \
  --input <modified_ONNX> \
  --pulsar2-output models/4_m1_cut_yoloe-26n-seg_640_pulsar2.onnx \
  --post-output models/4_m1_cut_yoloe-26n-seg_640_post.onnx \
  --check

# 5. Compile the front-end ONNX graph with Pulsar2
# Update input, calibration_dataset, and output_dir in the configuration first.
pulsar2 build --config pulsar2_u16_ax650.json

# 6. Run single-image inference on the AX650
# Place the script, axmodel, post-processing ONNX, class JSON, and test image together.
python3 e2e_infer_axmodel.py \
  --axmodel yoloe_26n_seg_m1.axmodel \
  --post-model 4_m1_cut_yoloe-26n-seg_640_post.onnx \
  --class-names yoloe-26n-seg_class_names.json \
  --image test.jpg \
  --output results/test.jpg

# 7. Run inference on the COCO validation dataset on the AX650
python3 e2e_infer_axmodel.py \
  --axmodel yoloe_26n_seg_m1.axmodel \
  --post-model 4_m1_cut_yoloe-26n-seg_640_post.onnx \
  --class-names yoloe-26n-seg_class_names.json \
  --image val2017 \
  --output results/ax \
  --eval-score-threshold 0.001 \
  --full-output

# 8. Evaluate COCO bbox/segm mAP on the development machine
python src/tool/evaluate_yoloe.py \
  --input results/ax/predictions.json \
  --ann datasets/annotations_val2017/annotations/instances_val2017.json \
  --class-names models/yoloe-26n-seg_class_names.json \
  --image-dir <full_val2017_image_directory> \
  --result-dir results/ax

# 9. Compare FP32 and axmodel metrics.json files
python src/tool/evaluate_yoloe.py \
  --compare-metrics \
  results/onnx/metrics.json \
  results/ax/metrics.json

Dataset inference defaults to quick performance mode and runs image reading, preprocessing, model inference, and the Host post-processing ONNX graph. To generate predictions.json for COCO evaluation, specify --full-output.

License: mit
Created: Sep 2, 2026
Updated: Sep 3, 2026