zh/models/yolov8/

[giscus[bot]]

zh/models/yolov8/

探索YOLOv8 的精彩功能，这是我们最新版本的实时物体检测器！了解先进的架构、预训练模型以及准确性和速度之间的最佳平衡如何使YOLOv8 成为您执行物体检测任务的最佳选择。

https://docs.ultralytics.com/zh/models/yolov8/

[onetree10]

作者您好！对于目标检测问题，在coco数据上的预训练模型和openv7上的预训练模型。分别选择两者对自己的数据集进行训练，①请问两者的数据集格式和存放路径有区别吗？②我准备应用在工业上设施缺陷检测，有8类缺陷、数据集大概2000张。有推荐的预训练模型吗？

[glenn-jocher]

@onetree10 嗨！看起来你想了解在训练过程中出现的0%| | 0/1689 [00:00<?, ?it/s]这是一个常见的显示问题，并不一定意味着实际出了问题。这通常在训练开始时短暂出现，表示正在准备数据或训练批次。如果训练能够继续进行，并能看到进度条更新，那么基本上就没有问题。👌

另外，关于你提到的准确率问题（Box(P 0.000245 R 0.0349 mAP50 0.000151），这看起来模型在初期学习阶段表现不佳。这可能是因为学习率设置得过高或过低，或者数据预处理/增强方式不适合你的数据集。可以尝试调整学习率或查看数据增强设置。🔧

针对工业缺陷检测，探索多种预训练模型真是个好办法。YOLOv8n通常是个不错的起点，因为它在速度和精度之间提供了不错的平衡。如果你的任务需要更高精度，可以尝试YOLOv8s或YOLOv8m。每个项目和数据集都有其独特性，最好的做法是进行实验，找到最适合你数据的配置。🚀

希望这些信息有用，祝你的项目成功！如果还有其他问题或需要进一步帮助，请随时告诉我们！

[onetree10]

libpng warning: iCCP: known incorrect sRGB profile
libpng warning: iCCP: known incorrect sRGB profile
libpng warning: iCCP: known incorrect sRGB profile
libpng warning: iCCP: known incorrect sRGB profile
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 36/36 [00:04<00:00, 7.91it/s]
all 141 365 0.000303 0.0265 0.000185 5.46e-05

  Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size

请问libpng warning: iCCP: known incorrect sRGB profile是什么问题呀

[glenn-jocher]

The libpng warning: iCCP: known incorrect sRGB profile message you're encountering is related to the PNG images being used. It indicates that these images contain ICC profile metadata that libpng (the library that reads and writes PNG files) considers to be incorrect or malformed. This is usually not a critical issue and more of a warning indicating potential misinterpretation of the color space by some software.

This warning typically does not affect the training process or model performance in object detection tasks with YOLOv8. If you want to remove these warnings and ensure consistent color profiles across your images, you might consider using a tool like ImageMagick to strip or correct the ICC profiles in your PNG files:

mogrify -strip path/to/images/*.png

This command removes all profiles and other unnecessary data from the images, potentially resolving the warning. Just replace path/to/images/ with your actual directory.

👍 Happy training with YOLOv8! If you have further questions or encounter other issues, feel free to reach out.

[onetree10]

是我数据图片格式的问题吗？我的是png格式，我将图片转换成jpg格式还会出现这个问题吗？

[glenn-jocher]

Hey there! 😊 Yes, changing the image format from PNG to JPG might help eliminate the libpng warning: iCCP: known incorrect sRGB profile warnings, as these are specific to some PNG files due to how they're encoded with color profiles. JPEGs don't use the same ICC color profiling system in the same way, so you shouldn't encounter this specific warning after converting.

To convert PNG images to JPG, you can use a quick script with popular Python libraries like Pillow. Here's a simple example to get you started:

from PIL import Image
import os

# Directory containing your PNG images
input_dir = 'path/to/your/png/images'
# Directory where you want to save JPG images
output_dir = 'path/to/save/jpg/images'

for filename in os.listdir(input_dir):
    if filename.endswith('.png'):
        img_path = os.path.join(input_dir, filename)
        img = Image.open(img_path)
        output_path = os.path.join(output_dir, filename.replace('.png', '.jpg'))
        
        # Convert the image to RGB mode and save it as a JPEG
        img.convert('RGB').save(output_path, 'JPEG')

print("Done converting images!")

Just make sure the output directory exists before running the script. This conversion might solve the warning issue and clear the way for smooth training or inference with YOLOv8 on your images. If you still encounter issues or have more questions, feel free to ask. Happy coding!

[haolele123]

作者您好，我想训练yolov8m-p2的版本，我在想能否使用yolov8的模型权重作为预训练权重，且我发现似乎没有yolov8-p2的m的版本这个怎末决解呢

[pderrenger]

您好！😊 感谢您对YOLOv8模型的兴趣！

对于使用YOLOv8的模型权重作为预训练权重，这是完全可行的，特别是如果您想要训练与已有模型结构相似的版本时。对于您提到的yolov8m-p2的版本，如果在我们提供的预训练模型中没有直接找到，这表示可能该具体模型版本目前不直接可用。

您可以尝试使用最相近的预训练模型开始训练，例如使用yolov8m.pt，并在训练命令中指定该预训练模型路径。这里有一个示例代码：

yolo train data=your_dataset.yaml model=yolov8m.pt epochs=100 imgsz=640

如果您的任务特别需要p2版本并且希望定制模型，请参考我们的文档和源代码进一步了解如何自定义模型结构或调整训练配置。

希望这有所帮助！如果还有其他问题，欢迎随时提问。祝您训练顺利！🚀

[Wenlong914]

有没有人能为我解答一下，训练数据集过程中的权重文件也能用吗？为什么训练过程中的权重文件比训练结束后的大？

[pderrenger]

您好！在训练过程中保存的权重文件通常会比训练完成后的权重文件大，这是因为它们可能包含额外的信息，比如优化器状态和用于继续训练的其他元数据。完成训练后，通常只会保存用于推断的模型参数，不包含训练期间需要的额外信息。

对于在训练过程中的权重文件，您完全可以使用它们来继续训练或进行预测，这在处理意外中断的训练时特别有用。如果您想验证模型的性能，或者希望进行一些测试，加载这些权重然后运行验证或推理是非常合适的。

希望这能帮助您！如果有更多问题，欢迎随时提问。🙂

[AntyRia]

Hi, thank you for your great contribution, this project is very useful!

I had A problem when I trained a model A using my own dataset (the tags in the train_classes.txt dataset are in the order of person,head,car, in the corresponding yaml file: 0,1, and 2 correspond to person,head,car, respectively. If I now want to reinforce model A with the new data set, must the order in my train_classes.txt be the same as the order of the tags in the first training? Or do I just need to make sure that train_classes.txt is the same as the corresponding yaml file?

[pderrenger]

@AntyRia hi there! Thank you for your kind words and for using our project! 😊

Regarding your question about reinforcing model A with a new dataset:

When you want to continue training or fine-tune your model with new data, it's crucial that the class order in your train_classes.txt file matches the order specified in your corresponding YAML file. This ensures that the labels are correctly aligned with the model's expectations.

To clarify:

• The order of classes in train_classes.txt should be consistent with the order in the YAML file used during the initial training.
• This consistency helps maintain the integrity of the class labels and ensures that the model correctly interprets the data during further training.

If you have any further questions or need additional assistance, feel free to ask. Happy training! 🚀

For more detailed guidance, you can always refer to our documentation.

[3154067760]

Hello author! For classification problems, model. rain (data=) data can only fill in the file path and cannot fill in YAML files. Why is that

[pderrenger]

@3154067760 hello! For classification tasks in YOLOv8, the data parameter in model.train() should indeed point to a YAML file that specifies the dataset configuration. If you're encountering issues, please ensure you're using the latest version of the Ultralytics package. If the problem persists, feel free to provide more details so we can assist further. For more information, you can refer to the Ultralytics documentation.

[3154067760]

作者你好！在yolov8处理10分类问题中我使用了train文件发生了报错
model = YOLO(r"yolov8-cls_mobilenet.yaml")#.load() # build from YAML and transfer weights
model.train(data=r"figer.yaml", epochs=5, imgsz=224)
报错如下：
File "E:\python_code\ultralytics-8.2.74\ultralytics\engine\trainer.py", line 533, in get_dataset
raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e
RuntimeError: Dataset 'figer.yaml' error [WinError 3] : 'E:\python_code\ultralytics-8.2.74\datasets\figer.yaml\train'
是不是data里面只能放文件夹的名字呢，不能给yaml文件

[pderrenger]

@3154067760 你好！报错信息显示数据集路径有问题。请确保figer.yaml文件中正确指定了训练数据集的路径，并且路径指向的是包含图像文件的文件夹，而不是单独的YAML文件。如果问题仍然存在，请更新到最新版本的Ultralytics库并重试。详细信息可以参考我们的文档。

[gaiya12138]

您好，能不能介绍一下YOLOv8，以及它和YOLOv5的区别。

[AntyRia]

去看论文

[glenn-jocher]

@AntyRia yOLOv8 is the latest version in the YOLO series, offering improved accuracy and speed with advanced features like an anchor-free architecture. Compared to YOLOv5, YOLOv8 provides better performance through optimized backbone and neck architectures. For more details, you can explore the YOLOv8 documentation.

[yooyo-Q]

Hello，How should I use the YOLOv8 segmentation algorithm to train a dataset with PNG label types?Thank you

[glenn-jocher]

@younkun to train a YOLOv8 segmentation model with PNG label types, ensure your dataset is correctly formatted and referenced in the YAML configuration file. You can then use the following command to start training:

yolo segment train data=your_dataset.yaml model=yolov8n-seg.pt epochs=100 imgsz=640

For more detailed guidance, please refer to the Ultralytics documentation.

[yunduandege]

yolov8在进行速度估计的时候，源码直接采用像素坐标来除以时间得到速度，这样的速度估算准确性如何？不考虑坐标变换等因素吗？希望作者能解答一下，有点看不明白这个逻辑。

[glenn-jocher]

@yunduandege the speed estimation using pixel coordinates divided by time is a simplified approach and may not account for factors like coordinate transformations. For more accurate results, consider incorporating additional data such as camera calibration and real-world scaling factors. If you have further questions, feel free to explore our documentation for more insights.

[maxgameone]

您好，请问下v8 如何实现多模型组合识别，我看v5上面有个组合 v8能组合模型吗

[glenn-jocher]

您好，YOLOv8 目前不支持直接组合多个模型进行识别。您可以在应用层面实现多个模型的串联或并行处理。感谢您对YOLO的关注！如需更多帮助，请查看我们的文档。

[yisi-code]

English : Hello, I am writing a paper on the improvement method of a module in YOLOv8 in the target detection task. May I ask how the performance metrics of YOLOv8n on the COCO dataset for object detection tasks are obtained from the official website? For example, under what experimental conditions and hyperparameter settings? If I train a new YOLOv8n weight from scratch on the COCO2017 dataset, can it represent the performance results of YOLOv8n on my machine under my experimental conditions and hyperparameter settings? Can you compare it with the performance results obtained from my improved YOLOv8n trained under the same conditions and include the comparison results in my paper?

Chinese : 您好，我正在写一篇关于YOLOv8中某一模块在目标检测任务上的改进方法论文。请问官网中对于目标检测任务给出的YOLOv8n在COCO数据集上的性能指标结果是如何得出的？例如在什么样的实验条件及超参设置等？如果我自己在COCO2017数据集上从头开始训练一个新的YOLOv8n权重，是否能够代表YOLOv8n在COCO2017数据集上并在我的实验条件及超参设置下在我的机器上的性能结果？能否将其与相同条件下训练的我改进后的YOLOv8n得到的性能结果进行比较并将比较结果放入我的论文中？

[glenn-jocher]

@yisi-xj the performance metrics for YOLOv8n on the COCO dataset are obtained under specific experimental conditions and hyperparameters, typically detailed in the model's documentation. Training a new YOLOv8n model on your setup will reflect performance under your conditions, allowing for comparison with your improved version. Feel free to include these comparisons in your paper. For more detailed information, please refer to the YOLOv8 documentation.

[yisi-code]

Thanks for the reply. I noticed a typo in the English part of my question above. “Can you compare it with the performance results obtained from my improved YOLOv8n trained under the same conditions and include the comparison results in my paper?” I originally wanted to say "Can i compare it with the performance results obtained from my improved YOLOv8n trained under the same conditions and include the comparison results in my paper?". Thanks again for your reply.

[glenn-jocher]

@yisi-xj you can certainly compare the performance of your improved YOLOv8n model with the baseline YOLOv8n under the same conditions. This comparison is valid and can be included in your paper to highlight the improvements. For specific experimental conditions and hyperparameters used in official benchmarks, please refer to the YOLOv8 documentation.

[lzh590]

yolov8训练结果精确率图像一直波动大如何解决，网上的好多方法都尝试了，还是没有解决

[glenn-jocher]

@lzh590 to address fluctuations in training accuracy, consider adjusting your learning rate, batch size, or data augmentation techniques. You might also want to experiment with different optimizer settings. For more detailed guidance, please refer to the YOLOv8 documentation.

[qiqibaba770]

Dear author, I set 500 epochs to be trained when I used yolov8 for training, but the model did not converge after the training was completed. I want to continue training from the 501st epochs, and the hyperparameters in the model training process can also be adjusted from the 501st epochs. Can you tell me how to do it?

[glenn-jocher]

@l1438 to continue training from the 501st epoch and adjust hyperparameters, load your last checkpoint using the resume option and specify any new hyperparameters you wish to modify.

[WindyYueYi]

Hello! As far as I know, YOLOv8Seg is trained using preconfigured image files (.jpg), label files (.txt), and a configuration file (.yaml) for training. Is it possible to train YOLOv8Seg using a real-time data stream? What I mean is that I have a pre-existing data generation program that can generate batches of images and corresponding location labels for objects to be segmented. How can I integrate this into YOLOv8Seg for real-time data stream training?Thank you!

[glenn-jocher]

@WindyYueYi training YOLOv8Seg with a real-time data stream isn't directly supported. However, you can modify the training loop to feed batches from your data generation program. Consider using a custom data loader in PyTorch to handle the streaming data. For more details, refer to the PyTorch documentation on custom datasets and data loaders.

[Smith-jeff]

Hello,may I ask which datasets and configurations were used during the training of yolov8n-pose？ I want to build my own dataset to improve the accuracy of yolov8n-pose on small objects and complex poses. However, after fine-tuning yolov8n_pose, I noticed a decrease in pose accuracy. This has led me to want to reproduce yolov8n-pose to gain more experience. Looking forward to your reply.

[glenn-jocher]

For training YOLOv8n-pose, we used the COCO dataset with specific configurations tailored for pose estimation. To improve accuracy on small objects and complex poses, consider adjusting hyperparameters and augmentations. For more details, please refer to the YOLOv8 documentation.

[yuanshuai666]

作者您好，yolov8 tracking(比如yolov8+bytetrack)支持检测一帧，跟踪多帧吗？

[glenn-jocher]

@yuanshuai666 yes, YOLOv8 tracking supports this through its integration with BoT-SORT and BYTETrack. You can achieve "detect once, track multiple frames" behavior by adjusting the tracker config settings. For implementation details, see the Object Tracking documentation which explains how to configure detection intervals and tracking persistence.

Example usage:

from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.track(source="video.mp4", tracker="bytetrack.yaml")

[yuanshuai666]

Thank you for the author's response. I checked the bytetrack.yaml file but did not find any parameters that could be set for detection intervals and tracking persistence. Could you provide me with an example or an explanation? The parameters are as follows:

Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

Default Ultralytics settings for ByteTrack tracker when using mode="track"

For documentation and examples see https://docs.ultralytics.com/modes/track/

For ByteTrack source code see https://github.com/ifzhang/ByteTrack

tracker_type: bytetrack # tracker type, ['botsort', 'bytetrack']
track_high_thresh: 0.25 # threshold for the first association
track_low_thresh: 0.1 # threshold for the second association
new_track_thresh: 0.25 # threshold for init new track if the detection does not match any tracks
track_buffer: 30 # buffer to calculate the time when to remove tracks
match_thresh: 0.8 # threshold for matching tracks
fuse_score: True # Whether to fuse confidence scores with the iou distances before matching

min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now)

[glenn-jocher]

For detection interval control in YOLOv8 tracking, you'll need to implement custom frame sampling logic outside the tracker configuration. The current BYTETrack integration focuses on association parameters rather than detection frequency. We recommend checking our Object Tracking documentation for implementation patterns and consider using a frame counter to skip detections periodically while letting the tracker handle inter-frame associations.

For commercial implementations requiring advanced tracking features, please contact our licensing team to discuss Enterprise License options that include enhanced tracking capabilities.

[lzh590]

这个我没有尝试过抱歉

…

---Original--- From: "Shuai ***@***.***&gt; Date: Mon, Feb 24, 2025 22:25 PM To: ***@***.***&gt;; Cc: ***@***.******@***.***&gt;; Subject: Re: [ultralytics/ultralytics] zh/models/yolov8/ (Discussion #10041) 作者您好，yolov8 tracking(比如yolov8+bytetrack)支持检测一帧，跟踪多帧吗？ — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: ***@***.***&gt;

[glenn-jocher]

@lzh590 yOLOv8's Track mode supports multi-object tracking across frames when combined with trackers like BYTETrack. You can configure the tracking parameters to balance detection frequency and tracking persistence. For implementation details, see the Track mode documentation at https://docs.ultralytics.com/modes/track/.

[CCCCHIRS]

作者你好，我想把YOLOv8训练完后的P-R曲线里面的字体改成times new roma字体，应该怎么样设置呢？

[glenn-jocher]

您可以通过在验证后绘制结果时修改Matplotlib的rcParams来设置字体。在代码中添加：

import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'Times New Roman'

然后运行model.val()生成新的图表。如果遇到字体加载问题，请确保系统已安装该字体。具体可参考Matplotlib的字体配置文档。

[CCCCHIRS]

作者你好，我想把YOLOv8训练完后的P-R曲线里面的字体改成times new roma字体，应该怎么样设置呢？

[glenn-jocher]

@CCCCHIRS to customize the font in YOLOv8's P-R curves, you can modify matplotlib's rcParams before plotting. Add this to your code:

import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'Times New Roman'

This will set Times New Roman as the default font for all matplotlib plots, including the validation curves generated during training. For more details on working with validation results, see the YOLOv8 Validation documentation.

[lihuihui12]

你好，我想问一下我好像在github上找不到yolo系列的源码了，除了v3 v5 和v11

[glenn-jocher]

@lihuihui12 您好！所有YOLO系列模型源码都统一维护在Ultralytics主仓库的ultralytics/ultralytics中。YOLOv8、YOLOv9、YOLOv10等模型实现都整合在该仓库的代码架构里，您可以通过查看模型文档了解各版本的具体实现细节和使用方法。

[ddd200716]

Dear Ultralytics author, I want the code for yolov8, but I couldn't find the file. All the files on Ultralytics' official GitHub are for yolov11. If possible, please provide me with the zip packaged code for yolov8.Look forward to your reply.

@lihuihui12 您好！所有YOLO系列模型源码都统一维护在Ultralytics主仓库的ultralytics/ultralytics中。YOLOv8、YOLOv9、YOLOv10等模型实现都整合在该仓库的代码架构里，您可以通过查看模型文档了解各版本的具体实现细节和使用方法。

[yuanshuai666]

Dear Author,
How can we avoid accuracy loss when converting a .pt model to an engine model?
Some objects can be detected with the .pt model, but after converting to the engine model, they can no longer be recognized.

[glenn-jocher]

@yuanshuai666 to minimize accuracy loss when converting a .pt model to an engine format like TensorRT, ensure you:

1. Use identical input resolutions during conversion and inference
2. Maintain FP32 precision (avoid FP16/INT8 quantization if accuracy is critical)
3. Verify your TensorRT/Optimizer versions match Ultralytics requirements

For detailed optimization guidance, see the Ultralytics Export documentation.

[ddd200716]

Hello author, I want the code for yolov8, but I couldn't find the file. When I go to Ultralytics' official GitHub, it's all for yolov11. If it's convenient, please give me the zip packaged code for yolov8.
作者您好，我想要yolov8的代码，但是我没有找到文件，进入Ultralytics官方的GitHub上面都是yolov11的，如果方便的话，请给我yolov8的zip打包的代码。

[Wenlong914]

11包含了8，我前天就使用yolo11最新包跑出来yolov8模型 陈ccc ***@***.***

…

---Original--- From: ***@***.***&gt; Date: Wed, Jun 11, 2025 11:36 AM To: ***@***.***&gt;; Cc: ***@***.******@***.***&gt;; Subject: Re: [ultralytics/ultralytics] zh/models/yolov8/ (Discussion #10041) Hello author, I want the code for yolov8, but I couldn't find the file. When I go to Ultralytics' official GitHub, it's all for yolov11. If it's convenient, please give me the zip packaged code for yolov8. 作者您好，我想要yolov8的代码，但是我没有找到文件，进入Ultralytics官方的GitHub上面都是yolov11的，如果方便的话，请给我yolov8的zip打包的代码。 — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: ***@***.***&gt;

[ddd200716]

您好，我想在yolov8的基础上面做改进，请问您有yolov8代码包嘛？通过yolov11来调用yolov8我不知道行不行。 Hello, I would like to make improvements on the basis of YOLOV8. Do you have the YOLOV8 code package? I don't know if calling YOLOV8 through YOLOV11 is feasible.

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------------------&nbsp;原始邮件&nbsp;------------------ 发件人: "ultralytics/ultralytics" ***@***.***&gt;; 发送时间:&nbsp;2025年6月11日(星期三) 中午11:48 ***@***.***&gt;; 抄送:&nbsp;"长安大学 ***@***.******@***.***&gt;; 主题:&nbsp;Re: [ultralytics/ultralytics] zh/models/yolov8/ (Discussion #10041) 11包含了8，我前天就使用yolo11最新包跑出来yolov8模型 陈ccc ***@***.***

---Original--- From: ***@***.***&amp;gt; Date: Wed, Jun 11, 2025 11:36 AM To: ***@***.***&amp;gt;; Cc: ***@***.******@***.***&amp;gt;; Subject: Re: [ultralytics/ultralytics] zh/models/yolov8/ (Discussion #10041) Hello author, I want the code for yolov8, but I couldn't find the file. When I go to Ultralytics' official GitHub, it's all for yolov11. If it's convenient, please give me the zip packaged code for yolov8. 作者您好，我想要yolov8的代码，但是我没有找到文件，进入Ultralytics官方的GitHub上面都是yolov11的，如果方便的话，请给我yolov8的zip打包的代码。 — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: ***@***.***&amp;gt; — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: ***@***.***&gt;

[YWJ-sudo]

我最近使用yolo时发现即便我指定了yolov8n的模型参数但是在训练时它依然会自动下载yolov11n的参数，那么如何避免这种情况去使用yolov8呢？

[fangyuZ1025]

直接调用model=yolov8n就好了 月寒日暖煎人寿 ***@***.***

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------------------ Original ------------------ From: YWJ-sudo ***@***.***&gt; Date: Mon,Jul 28,2025 10:33 AM To: ultralytics/ultralytics ***@***.***&gt; Cc: fangyuZ1025 ***@***.***&gt;, Mention ***@***.***&gt; Subject: Re: [ultralytics/ultralytics] zh/models/yolov8/ (Discussion #10041) 我最近使用yolo时发现即便我指定了yolov8n的模型参数但是在训练时它依然会自动下载yolov11n的参数，那么如何避免这种情况去使用yolov8呢？ — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: ***@***.***&gt;

[YWJ-sudo]

I see, thank you for your reply. 冷落光明 ***@***.*** &nbsp;

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------------------&nbsp;原始邮件&nbsp;------------------ 发件人: "ultralytics/ultralytics" ***@***.***&gt;; 发送时间:&nbsp;2025年7月29日(星期二) 下午5:33 ***@***.***&gt;; ***@***.******@***.***&gt;; 主题:&nbsp;Re: [ultralytics/ultralytics] zh/models/yolov8/ (Discussion #10041) 直接调用model=yolov8n就好了 月寒日暖煎人寿 ***@***.***

------------------ Original ------------------ From: YWJ-sudo ***@***.***&amp;gt; Date: Mon,Jul 28,2025 10:33 AM To: ultralytics/ultralytics ***@***.***&amp;gt; Cc: fangyuZ1025 ***@***.***&amp;gt;, Mention ***@***.***&amp;gt; Subject: Re: [ultralytics/ultralytics] zh/models/yolov8/ (Discussion #10041) 我最近使用yolo时发现即便我指定了yolov8n的模型参数但是在训练时它依然会自动下载yolov11n的参数，那么如何避免这种情况去使用yolov8呢？ — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: ***@***.***&amp;gt; — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: ***@***.***&gt;

[heziqi-center]

Hello dear author, I used yolov8 for fire and smoke and the results of box(p R as well as MAP50 for fire are 0.937 0.84 0.91 but for smoke is only 0.805 0.526 and 0.625, what should I do next?

[heziqi-center]

Hello dear author, I used yolov8 for fire and smoke and the results of box(p R as well as MAP50 for fire are 0.937 0.84 0.91 but for smoke is only 0.805 0.526 and 0.625, what should I do next?

[zzw725]

Hello author!
1.Can overfitting caused by a single background be effectively prevented by increasing the training background？
2. what is the proportion of background that needs to be increased?
Thanks！

[YWJ-sudo]

coco数据集中的背景占比或许可以作为一个参考

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---Original--- From: ***@***.***&gt; Date: Mon, Aug 18, 2025 11:45 AM To: ***@***.***&gt;; Cc: ***@***.******@***.***&gt;; Subject: Re: [ultralytics/ultralytics] zh/models/yolov8/ (Discussion #10041) Hello author! 1.Can overfitting caused by a single background be effectively prevented by increasing the training background？ 2. what is the proportion of background that needs to be increased? Thanks！ — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: ***@***.***&gt;

[zzw725]

yes, I know. However, the information about the background proportion of the coco dataset was not queried.

[glenn-jocher]

There’s no official “background proportion” for COCO—it varies by image and isn’t reported; in practice, add a small share of background-only images as hard negatives and tune it by monitoring validation false positives (a 5–20% starting range is common), and note that Ultralytics YOLO accepts images with empty/no labels as negatives; for setup and tuning details, see the Train mode docs and the Hyperparameter Tuning guide.