zh/tasks/detect/

[giscus[bot]]

zh/tasks/detect/

YOLOv8 的官方文档，由Ultralytics 提供。了解如何以各种格式训练、验证、预测和导出模型。包括详细的性能统计。

https://docs.ultralytics.com/zh/tasks/detect/

[LGxaz]

Traceback (most recent call last):
File "D:\Anaconda\anaconda3\envs\yolo_py38\lib\runpy.py", line 194, in _run_module_as_main
return run_code(code, main_globals, None,
File "D:\Anaconda\anaconda3\envs\yolo_py38\lib\runpy.py", line 87, in run_code
exec(code, run_globals)
File "D:\Anaconda\anaconda3\envs\yolo_py38\Scripts\yolo.exe_main.py", line 7, in
File "F:\yolov8\ultralytics\ultralytics\cfg_init.py", line 582, in entrypoint
getattr(model, mode)(**overrides) # default args from model
File "F:\yolov8\ultralytics\ultralytics\engine\model.py", line 668, in train
self.trainer.train()
File "F:\yolov8\ultralytics\ultralytics\engine\trainer.py", line 198, in train
self._do_train(world_size)
File "F:\yolov8\ultralytics\ultralytics\engine\trainer.py", line 418, in _do_train
self.metrics, self.fitness = self.validate()
File "F:\yolov8\ultralytics\ultralytics\engine\trainer.py", line 557, in validate
metrics = self.validator(self)
File "D:\Anaconda\anaconda3\envs\yolo_py38\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "F:\yolov8\ultralytics\ultralytics\engine\validator.py", line 195, in call
stats = self.get_stats()
File "F:\yolov8\ultralytics\ultralytics\models\yolo\detect\val.py", line 170, in get_stats
stats = {k: torch.cat(v, 0).cpu().numpy() for k, v in self.stats.items()} # to numpy
File "F:\yolov8\ultralytics\ultralytics\models\yolo\detect\val.py", line 170, in
stats = {k: torch.cat(v, 0).cpu().numpy() for k, v in self.stats.items()} # to numpy
RuntimeError: torch.cat(): expected a non-empty list of Tensors

[pderrenger]

Hey there 👋, it looks like you're encountering a RuntimeError related to torch.cat(): it expects a non-empty list of tensors but received an empty one. This error often occurs when the model doesn't detect any objects in the validation set, leading to an empty list of results being passed to torch.cat().

To troubleshoot, consider the following steps:

• Ensure your dataset is properly loaded and accessible to YOLOv8 during validation.

• Check if the provided data path in your config file points to valid dataset directories.

• Temporarily reduce the conf threshold in your training script to see if it starts detecting objects. For example, lowering it to 0.01 might help:

  yolo train data=coco128.yaml model=yolov8n.yaml conf=0.01

If the issue persists, it could be helpful to share more details about your setup, including the dataset and configuration files, so we can offer more targeted advice! 😊

[LGxaz]

感谢回复，已经解决，问题在于数据集xml转yolo失败。

[pderrenger]

太棒了，很高兴听到您解决了问题！🎉 数据集转换问题确实会导致一些头疼的错误。如果未来您有任何关于模型训练或部署的疑问，或者需要关于数据准备的更多建议，随时欢迎您回来提问。🙂

下次遇到XML转YOLO格式失败时，可以尝试使用我们提及的JSON2YOLO工具来简化流程。这个工具能帮助您轻松转换不同格式的数据集。

祝您在接下来的项目中一切顺利！

[onetree10]

Does the YOLOv8 pre trained model for object detection support training its own dataset (the dataset format is converted from polygon annotated JSON format to YOLO format)

[glenn-jocher]

Yes, the YOLOv8 pre-trained model supports training on your own dataset, even if it was originally in a different annotation format like polygon annotated JSON. You'll need to convert your dataset to the YOLO format first. Once your dataset is in YOLO format, you can use it for training by specifying your dataset in the data YAML file and then proceeding with training as usual.

Here's a quick example on how to train with your custom dataset after conversion:

# Load your model and train on your custom dataset
yolo detect train data=your_dataset.yaml model=yolov8n.pt epochs=100 imgsz=640

Make sure your your_dataset.yaml correctly points to your training and validation data in the YOLO format. Happy training! 🚀

[onetree10]

I have now annotated a batch of data (in JSON format) using the polygon annotation tool, which I want to use for object detection tasks. Does YOLOv8's object detection pre-trained model support polygon-annotated data? YOLOv5 does not support polygon-annotated data. (Or YOLOv8 supports polygon annotation data, pay attention to the separation from the instance when converting json to YOLO?)

[glenn-jocher]

Hey there! 👋 YOLOv8, similar to YOLOv5, fundamentally works with bounding boxes for object detection and doesn't natively support polygon annotations directly in its training process. But, don't worry! You can convert your polygon-annotated data into bounding boxes following a simple approach:

1. Convert your polygon annotations to the bounding box format by calculating the minimum and maximum coordinates enclosing your polygon.

For example, if you have a polygon annotation in JSON:

{
  "shapes": [
    {
      "label": "YourObject",
      "points": [[x1, y1], [x2, y2], [x3, y3], [x4, y4]]
    }
  ]
}

Convert this to a bounding box by finding the min and max of x and y coordinates.

2. Once converted to bounding boxes, format your data in the YOLO format:

<object-class> <x_center> <y_center> <width> <height>

Make sure to normalize x_center, y_center, width, and height by the image width and height to convert them into relative coordinates.

Remember, the transition from polygons to bounding boxes might not capture the exact essence of very irregular shapes but works well for a wide range of applications. Train your model as usual after conversion! 🚀

Let us know if you need any further assistance. Happy training!

[onetree10]

JSON2YOLO the "general_json2yolo" and "labelbox_json2yolo" in the tool, the two scripts are respectively the JSON format of the dataset of the task and the YOLO format of the dataset of the task (for example, the format of the JSON dataset of instance segmentation is accurately changed to the YOLO dataset format of the target detection).

[glenn-jocher]

Hey there! 👋 It looks like you're exploring the tools general_json2yolo and labelbox_json2yolo for converting JSON dataset formats to YOLO format, specifically for instance segmentation to object detection tasks. That's an exciting step towards customizing your models! 🚀

When converting, ensuring the bounding boxes derived from your polygon annotations accurately encompass your objects of interest is crucial. This conversion process retains essential object details while complying with YOLO's detection format.

Here's a quick Python code snippet illustrating how you might convert JSON polygon annotations to YOLO format bounding boxes:

# Assuming 'annotations' is your list of polygon annotations from JSON
for annotation in annotations:
    # Find the min and max coordinates to form a bounding box
    x_coordinates = [point[0] for point in annotation['points']]
    y_coordinates = [point[1] for point in annotation['points']]
    bbox = [min(x_coordinates), min(y_coordinates), max(x_coordinates), max(y_coordinates)]
    
    # Convert to YOLO format (x_center, y_center, width, height)
    bbox_yolo = convert_to_yolo_format(bbox, image_width, image_height)
    
    # Save or use the bbox_yolo for your YOLO training data

Remember, converting annotations formats might require some tweaks depending on the specifics of your dataset. Always check to ensure the converted bounding boxes are accurate and represent your objects well.

Feel free to reach out if you have further questions or need more help. Happy training! 🤗

[gulanyun]

The results of the training are not available, metrics/mAP50(B), metrics/mAP50-95(B),always too small

[glenn-jocher]

@gulanyun it seems like your training results are showing low mAP scores. To improve this, consider checking your dataset quality, ensuring proper annotation, and adjusting hyperparameters like learning rate or batch size. For more detailed guidance, you might want to explore the Ultralytics documentation on training strategies.

[xiao5676wen]

对视频进行检测时，设定参数save=Ture，但是保存的时一个0字节的avi格式视频，我怎样才能顺利的保存视频，而且我能不能用参数指定保存的视频格式？

[xiao5676wen]

检测视频后，保存检测视频却保存出一个0字节的avi格式视频！我想顺利保存出正确的视频，并且希望能用预测时的参数指定我想保存的视频格式。

[glenn-jocher]

@xiao5676wen 在视频检测保存为0字节的情况下，可能与编码器设置或输出路径权限有关。请确保 ffmpeg 安装正确，因为它是保存视频的依赖工具。同时，参数 save 无法直接指定格式，默认保存为 .mp4。如需其他格式，建议后处理时用工具如 ffmpeg 转换格式。运行时，检查日志是否有错误提示，帮助排查问题。