zh/guides/sahi-tiled-inference/

[giscus[bot]]

zh/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.

https://docs.ultralytics.com/zh/guides/sahi-tiled-inference/

[bqpeng]

Hi, inquiry. is SAHI only used in the reasoning stage? For example, if I have a customized dataset and train it, I still train it normally and get the pt file. Then add SAHI and use the trained pt file for inference, still for training, I need to add SAHI as well.

[glenn-jocher]

@bqpeng sAHI is used only during the inference stage. You train your model normally and then apply SAHI for inference to handle large images efficiently. For more details, please refer to the SAHI guide.

[bqpeng]

thank you！

[glenn-jocher]

@bqpeng thank you for your interest! SAHI is used only during the inference stage to optimize detection on large images. You can train your model as usual and then apply SAHI for efficient inference. For more details, please refer to the SAHI guide.

[mmoollllee]

However, you need to make sure that you are training square images that actually represent what SAHI will use for inference, right? Check out my tiling plugin for Voxel51 to tile your hq images for training.

[glenn-jocher]

Thank you for sharing! Yes, ensuring that your training images match the tiled inference setup used by SAHI is crucial for consistent results. Your Voxel51 tiling plugin looks like a valuable tool for preparing datasets for such workflows. For more on integrating SAHI with YOLO11, please refer to the official SAHI guide.

[UltralyticsAssistant]

👋 Hello, thank you for your interest in Ultralytics 🚀! We recommend checking out our comprehensive Documentation for guidance and insights. You'll find examples on using Python and CLI tools there, which may address many of your questions.

If you're encountering any issues with the SAHI tiled inference guide, please make sure to provide a minimum reproducible example if this is a 🐛 Bug Report, to help us understand and address it effectively.

For detailed questions or custom training inquiries ❓, include comprehensive information such as dataset image samples and training logs. Make sure you are following our Tips for Best Training Results.

Join the vibrant Ultralytics community where you feel most comfortable. Engage in real-time discussions on Discord 🎧, explore deep-dive topics on Discourse, or share insights on our Subreddit.

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[ZSBSB]

Your work is perfect, I have one question, does the current point SAHI library support yolov11's rotating frame target detection?

[fcakyon]

Yes it supports 👌🏻

[handsome2456]

Hi! How to utilize sahi for YOLOv8 instance segmentation?

[glenn-jocher]

@handsome2456 to utilize SAHI with YOLOv8 for instance segmentation, you can integrate them by installing the required libraries (pip install -U ultralytics sahi), loading a YOLOv8 model with AutoDetectionModel from SAHI, and performing sliced inference using get_sliced_prediction. You can find detailed implementation steps in the SAHI Tiled Inference guide. Let me know if you need further assistance!

[bearllm]

Does SAHI support Oriented Bounding Box?

[glenn-jocher]

@fcakyon could you answer @bearllm on SAHI support for YOLO11 OBB models?

[fcakyon]

Hello @bearllm, author of the SAHI here.

SAHI supports YOLO11 OBB models as of sahi==0.11.20 🚀

[zhugelaozei]

Hi，inquriy.How to perform inference on multiple slices simultaneously?I need to infer a so high resolution image and I want to maintain inference speed, but it is so slow because batch size and cuda is only used one.

[glenn-jocher]

@zhugelaozei to perform inference on multiple slices simultaneously and optimize the use of CUDA resources, you can use SAHI's batch prediction capabilities. This allows you to process multiple slices or images in parallel by leveraging batch size and slicing configurations. Refer to the SAHI documentation for batch prediction examples: https://docs.ultralytics.com/zh/guides/sahi-tiled-inference/. Ensure your hardware and CUDA setup are configured to handle parallel processing efficiently.

[wenxi2]

hi，is there code for evaluating the COCO metrics after cutting and stitching predictions?

[fcakyon]

@wenxi2 you can follow this guide for SAHI based YOLOv8/YOLO11/RTDETR evalution: obss/sahi#622

[wenxi2]

Thank you very much for your reply. I followed the tutorial for the evaluation and obtained such results, but the visualization effect has clearly improved.I am using the VisDrone dataset. Do you know the reason? 随便 ***@***.***  

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[fcakyon]

Please try decreasing the threshold to 0.001 to increase the COCO eval scores @wenxi2 .