Question About YOLOE-26 Result
Author: zixi01chenCreated Jul 31, 2026Updated Sep 14, 2026
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Thanks for the work YOLO26 https://arxiv.org/abs/2606.03748. I noticed that the result of YOLOE-26M result is 35.4 on LVIS minival dataset.
However, i use the code from https://docs.ultralytics.com/models/yolo26#supported-tasks-and-modes
from ultralytics import YOLOE
model = YOLOE("yoloe-26m-seg.pt") # or yoloe-26s/m-seg.pt for different sizes
metrics = model.val(data="lvis.yaml")
But I get result:
DONE (t=28.35s).
Accumulating evaluation results...
COCOeval_opt.accumulate() finished...
DONE (t=0.00s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 catIds=all] = 0.328
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 catIds=all] = 0.429
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 catIds=all] = 0.356
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 catIds=all] = 0.223
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 catIds=all] = 0.419
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 catIds=all] = 0.540
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 catIds= r] = 0.326
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 catIds= c] = 0.320
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 catIds= f] = 0.336
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 catIds=all] = 0.285
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 catIds=all] = 0.415
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 catIds=all] = 0.424
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 catIds=all] = 0.269
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 catIds=all] = 0.511
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 catIds=all] = 0.667
Average Recall (AR) @[ IoU=0.50 | area= all | maxDets=100 catIds=all] = 0.536
Average Recall (AR) @[ IoU=0.75 | area= all | maxDets=100 catIds=all] = 0.460
Evaluate annotation type *segm*
COCOeval_opt.evaluate() finished...
DONE (t=49.92s).
Accumulating evaluation results...
COCOeval_opt.accumulate() finished...
DONE (t=0.00s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 catIds=all] = 0.278
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 catIds=all] = 0.410
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 catIds=all] = 0.293
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 catIds=all] = 0.165
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 catIds=all] = 0.370
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 catIds=all] = 0.480
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 catIds= r] = 0.283
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 catIds= c] = 0.281
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 catIds= f] = 0.273
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 catIds=all] = 0.247
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 catIds=all] = 0.352
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 catIds=all] = 0.359
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 catIds=all] = 0.198
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 catIds=all] = 0.455
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 catIds=all] = 0.596
Average Recall (AR) @[ IoU=0.50 | area= all | maxDets=100 catIds=all] = 0.514
Average Recall (AR) @[ IoU=0.75 | area= all | maxDets=100 catIds=all] = 0.383It not equal to 35.4. I wonder how can I reproduce the YOLOE-26M Result? Thanks.
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Source: ultralytics/ultralytics