# Does SAHI with YOLO11 make sense **URL:** \ **Category:** Discussion\ **Tags:** discussion\ **Created:** [September 15, 2025, 7:05pm UTC](https://community.ultralytics.com/t/does-sahi-with-yolo11-make-sense/1465 "2025-09-15T19:05:09Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![Kallinteris-Andreas](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/kallinteris-andreas/32/838_2.png) [@Kallinteris-Andreas](https://community.ultralytics.com/u/Kallinteris-Andreas)\ **Post date:** [September 15, 2025, 7:05pm UTC](https://community.ultralytics.com/t/does-sahi-with-yolo11-make-sense/1465/1 "2025-09-15T19:05:09Z")
Is there a reason to use SAHI with YOLO11, even though YOLO11 runtime performance scales linearly with respect to input resolution Is it perhaps the multiscale feature extractor does not work well with big resolutions? ## Measurements Here is a summary of the performance metrics for each YOLO model at different input resolutions on an NVIDIA GeForce GTX 1650 Ti with TensorRT. ![image](https://us1.discourse-cdn.com/flex001/uploads/ultralytics1/original/1X/3659cf99a04accc6ade7f7fac31682492474fda6.png) ### **YOLOv10 Series Performance** | Model | Input Size | Avg. Preprocess Time (ms) | Avg. Inference Time (ms) | Avg. Postprocess Time (ms) | Avg. VRAM Used (MB) | | --- | --- | --- | --- | --- | --- | | **yolov10n** | (320, 320) | 1.2481 | 2.1734 | 0.8606 | 882.44 | | | (480, 480) | 1.8137 | 2.3863 | 0.5758 | 882.44 | | | (640, 640) | 2.8106 | 3.1079 | 0.5843 | 882.44 | | | (960, 960) | 5.7605 | 6.3184 | 0.5973 | 882.44 | | | (1280, 1280) | 9.3013 | 11.0719 | 0.5505 | 882.44 | | **yolov10s** | (320, 320) | 1.2377 | 3.1524 | 0.5724 | 1030.44 | | | (480, 480) | 1.8538 | 4.2261 | 0.5993 | 1030.44 | | | (640, 640) | 2.6764 | 6.3744 | 0.5907 | 1030.44 | | | (960, 960) | 5.7693 | 13.8813 | 0.5528 | 1030.44 | | | (1280, 1280) | 9.3562 | 25.7975 | 0.5460 | 1030.44 | | **yolov10m** | (320, 320) | 1.2352 | 5.6571 | 0.5924 | 1232.44 | | | (480, 480) | 1.8136 | 9.5817 | 0.5973 | 1232.44 | | | (640, 640) | 2.7824 | 15.3578 | 0.6080 | 1232.44 | | | (960, 960) | 5.6341 | 34.8607 | 0.6256 | 1232.44 | | | (1280, 1280) | 9.2966 | 61.3020 | 0.5545 | 1232.44 | | **yolov10b** | (320, 320) | 1.3440 | 7.3629 | 0.6265 | 1366.44 | | | (480, 480) | 1.8390 | 13.1377 | 0.6610 | 1366.44 | | | (640, 640) | 2.7249 | 21.8776 | 0.6012 | 1366.44 | | | (960, 960) | 5.6994 | 48.9557 | 0.6164 | 1366.44 | | | (1280, 1280) | 9.3457 | 86.7367 | 0.5631 | 1366.44 | | **yolov10l** | (320, 320) | 1.1543 | 9.3652 | 0.5938 | 1410.44 | | | (480, 480) | 1.8108 | 16.9321 | 0.6194 | 1410.44 | | | (640, 640) | 2.8090 | 28.5152 | 0.6220 | 1410.44 | | | (960, 960) | 5.5664 | 63.7005 | 0.6184 | 1410.44 | | | (1280, 1280) | 9.4435 | 112.4555 | 0.5680 | 1410.44 | | **yolov10x** | (320, 320) | 1.1555 | 13.1908 | 0.6079 | 1690.44 | | | (480, 480) | 1.7787 | 24.4779 | 0.6228 | 1690.44 | | | (640, 640) | 2.7059 | 41.6001 | 0.6006 | 1690.44 | | | (960, 960) | 5.7067 | 90.4771 | 0.5575 | 1690.44 | | | (1280, 1280) | 9.3776 | 157.9123 | 0.5594 | 1690.44 | ### **YOLOv11 Series Performance** | Model | Input Size | Avg. Preprocess Time (ms) | Avg. Inference Time (ms) | Avg. Postprocess Time (ms) | Avg. VRAM Used (MB) | | --- | --- | --- | --- | --- | --- | | **yolo11n** | (320, 320) | 1.2347 | 1.8521 | 1.8750 | 1014.44 | | | (480, 480) | 1.8643 | 2.4851 | 1.5385 | 1014.44 | | | (640, 640) | 2.7956 | 3.1183 | 1.5210 | 1014.44 | | | (960, 960) | 5.6465 | 6.0820 | 0.9226 | 1014.44 | | | (1280, 1280) | 9.2960 | 10.7876 | 0.9377 | 1014.44 | | **yolo11s** | (320, 320) | 1.2414 | 3.4239 | 1.5306 | 1080.44 | | | (480, 480) | 1.8326 | 4.2235 | 1.5452 | 1080.44 | | | (640, 640) | 2.8127 | 6.1560 | 1.5421 | 1080.44 | | | (960, 960) | 5.7622 | 13.2459 | 0.9688 | 1080.44 | | | (1280, 1280) | 9.2885 | 24.6474 | 1.1847 | 1080.44 | | **yolo11m** | (320, 320) | 1.1947 | 6.1232 | 1.5600 | 1376.44 | | | (480, 480) | 1.7759 | 9.9287 | 1.5427 | 1376.44 | | | (640, 640) | 2.7940 | 15.6612 | 1.5372 | 1376.44 | | | (960, 960) | 5.6145 | 35.5620 | 1.0836 | 1376.44 | | | (1280, 1280) | 9.2957 | 63.2755 | 1.3753 | 1376.44 | | **yolo11l** | (320, 320) | 1.2316 | 7.6474 | 1.5272 | 1424.44 | | | (480, 480) | 1.7991 | 12.9173 | 1.5550 | 1424.44 | | | (640, 640) | 2.7510 | 20.7198 | 1.5397 | 1424.44 | | | (960, 960) | 5.5351 | 46.7643 | 1.1085 | 1424.44 | | | (1280, 1280) | 9.2214 | 83.8281 | 1.3782 | 1424.44 | | **yolo11x** | (320, 320) | 1.2655 | 14.1565 | 1.5343 | 2056.44 | | | (480, 480) | 1.7940 | 26.1876 | 1.5846 | 2056.44 | | | (640, 640) | 2.7864 | 43.4137 | 1.5644 | 2056.44 | | | (960, 960) | 5.5922 | 98.3690 | 0.9486 | 2056.44 | | | (1280, 1280) | 9.3528 | 173.5436 | 1.2526 | 2056.44 | ###Note YOLO12 does have sublinear performance scalling with respect to input resolution, but due to a software issue i can not run it with TensorRT ## sources > **[Ultralytics Docs: Using YOLO11 with SAHI for Sliced Inference](https://docs.ultralytics.com/guides/sahi-tiled-inference/)** > > Learn how to implement YOLO11 with SAHI for sliced inference. Optimize memory usage and enhance detection accuracy for large-scale applications. ---
**Author:** ![BurhanQ](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/burhanq/32/7_2.png) [@BurhanQ](https://community.ultralytics.com/u/BurhanQ)\ **Post date:** [September 16, 2025, 12:27pm UTC](https://community.ultralytics.com/t/does-sahi-with-yolo11-make-sense/1465/2 "2025-09-16T12:27:50Z")
The primary use case for SAHI is when the objects to be detected are extremely small relative to the image dimensions. If the objects are \>= 20 pixels ^ 2 of a 640 x 640 image (object is roughly 3% of the image siz3), then it’s unlikely that there is a need for SAHI. The [original publication](https://arxiv.org/pdf/2202.06934) for the SAHI method describes using this method for images with objects \< 1% of the image width. There will always be a trade off when needing to increase image resolution for inference, and even more so if using sliced inference. You’ll need to decide what the threshold is for your use case and to test if it meets your intended goal. That said, I would use SAHI only in cases where inference speed is not highly critical. ---
**Author:** ![Kallinteris-Andreas](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/kallinteris-andreas/32/838_2.png) [@Kallinteris-Andreas](https://community.ultralytics.com/u/Kallinteris-Andreas)\ **Post date:** [September 17, 2025, 1:03pm UTC](https://community.ultralytics.com/t/does-sahi-with-yolo11-make-sense/1465/3 "2025-09-17T13:03:21Z")
I did not realize SAHI had a research paper associated with it, thanks for pointing it out The benefit from that I understand: This allows detecting small objects without fine-tuning a model to detect small objects, for example when inferencing 1280x1280 image with 640x640 detector effectively increased the size of the objects relative to the input image the object detector receives My follow up question is: Is it possible to train/fine-tune a model such as YOLO11 to natively be able to detection small objects while running at high resolutions (such as 1280x1280, 1920x1080, or even 3840x2060) by training it with high resolution images. Or is the training algorithm/model architecture not capable at scaling at those high input resolution effectively. Thank you very much! ---
**Author:** ![BurhanQ](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/burhanq/32/7_2.png) [@BurhanQ](https://community.ultralytics.com/u/BurhanQ)\ **Post date:** [September 18, 2025, 11:32am UTC](https://community.ultralytics.com/t/does-sahi-with-yolo11-make-sense/1465/4 "2025-09-18T11:32:09Z")
Yes you can! That said, you may run into hardware issues, as larger images will require more GPU and system memory. Even if you have sufficient hardware, training will take longer. You can train at practically any resolution you’d like, however it’s quite likely there will be a point of diminishing return. Unfortunately the only way to find where the threshold is, will be to conduct testing.