zh/datasets/classify/

[giscus[bot]]

zh/datasets/classify/

了解如何构建数据集 YOLO 分类任务。详细的文件夹结构和使用示例，用于有效培训。

https://docs.ultralytics.com/zh/datasets/classify/

[MrLi-creator]

Hi, how can I change the class weight in the training of yolov8n-cls.pt?

[pderrenger]

@MrLi-creator to change the class weights in the training of yolov8n-cls.pt, you can modify the class_weights parameter in the training configuration. This parameter allows you to assign different weights to each class, which can help in handling class imbalance. Ensure you are using the latest version of the Ultralytics package for the best compatibility and features. For more detailed instructions, you can refer to the Ultralytics documentation on classification datasets.

[MrLi-creator]

Hi pderrenger,
Firstly, thank you for your response. I am trying to add classw_weights to the data.yaml file. However, it was found that the classification task cannot use the data.yaml file, as you suggested in the link(#13789). So are there any other ways to modify classw_weights in classification, preferably with an example

[pderrenger]

@MrLi-creator hi pderrenger,

Thank you for your question. For modifying class weights in classification tasks, you can directly set the class_weights parameter in the training script. Unfortunately, the data.yaml file isn't used for classification tasks. Instead, you can specify class weights programmatically when initializing the training process. If you need further assistance, please refer to the Ultralytics documentation on classification datasets.

[MrLi-creator]

@pderrenger
Hi pderrenger. I have set class_weights parameter in the training script. But it report error.

results = model.train(data='./datasets', epochs=30, imgsz=50, device=None, batch=16,classes =[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0] )

SyntaxError: 'class_weights' is not a valid YOLO argument. Similar arguments are i.e. ['classes'].

[pderrenger]

Hi @MrLi-creator,

The class_weights parameter is not directly supported in the train method for YOLO classification tasks. Instead, you can handle class imbalance by adjusting the dataset or using techniques like data augmentation. If you encounter any issues, please ensure you are using the latest version of the Ultralytics package. For more detailed guidance, you can refer to the Ultralytics documentation on classification datasets.

[1ZZZyyyh]

that's why? I've made my dataset as the sample.

File "/home/visitor/anaconda3/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 567, in get_dataset
raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e
RuntimeError: Dataset '/home/visitor/yolov8-tb11' error ❌ [Errno 21] Is a directory: '/home/visitor/yolov8-tb11'

[glenn-jocher]

The error indicates that the data argument provided points to a directory rather than a valid dataset configuration file (.yaml). Ensure you specify the path to a correctly formatted data.yaml file that defines the dataset structure. For reference, visit Dataset Structure for YOLO Classification Tasks. Let me know if you need further clarification!

[1ZZZyyyh]

I want to train my model , it reports error.
yolo task=classify mode=train model=yolov8m-cls.pt data=/home/visitor/yolov8-tb11 epochs=100 imgsz=640
...
Traceback (most recent call last):
File "/home/visitor/anaconda3/bin/yolo", line 8, in
sys.exit(entrypoint())
^^^^^^^^^^^^
File "/home/visitor/anaconda3/lib/python3.12/site-packages/ultralytics/cfg/init.py", line 991, in entrypoint
getattr(model, mode)(**overrides) # default args from model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/visitor/anaconda3/lib/python3.12/site-packages/ultralytics/engine/model.py", line 806, in train
self.trainer.train()
File "/home/visitor/anaconda3/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 207, in train
self._do_train(world_size)
File "/home/visitor/anaconda3/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 323, in _do_train
self._setup_train(world_size)
File "/home/visitor/anaconda3/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 264, in _setup_train
self.amp = torch.tensor(check_amp(self.model), device=self.device)
^^^^^^^^^^^^^^^^^^^^^
File "/home/visitor/anaconda3/lib/python3.12/site-packages/ultralytics/utils/checks.py", line 707, in check_amp
assert amp_allclose(YOLO("yolo11n.pt"), im)
^^^^^^^^^^^^^^^^^^
File "/home/visitor/anaconda3/lib/python3.12/site-packages/ultralytics/models/yolo/model.py", line 23, in init
super().init(model=model, task=task, verbose=verbose)
File "/home/visitor/anaconda3/lib/python3.12/site-packages/ultralytics/engine/model.py", line 146, in init
self._load(model, task=task)
File "/home/visitor/anaconda3/lib/python3.12/site-packages/ultralytics/engine/model.py", line 289, in _load
self.model, self.ckpt = attempt_load_one_weight(weights)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/visitor/anaconda3/lib/python3.12/site-packages/ultralytics/nn/tasks.py", line 908, in attempt_load_one_weight
ckpt, weight = torch_safe_load(weight) # load ckpt
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/visitor/anaconda3/lib/python3.12/site-packages/ultralytics/nn/tasks.py", line 835, in torch_safe_load
ckpt = torch.load(file, map_location="cpu")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/visitor/anaconda3/lib/python3.12/site-packages/ultralytics/utils/patches.py", line 86, in torch_load
return _torch_load(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/visitor/anaconda3/lib/python3.12/site-packages/torch/serialization.py", line 1326, in load
with _open_zipfile_reader(opened_file) as opened_zipfile:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/visitor/anaconda3/lib/python3.12/site-packages/torch/serialization.py", line 671, in init
super().init(torch._C.PyTorchFileReader(name_or_buffer))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: PytorchStreamReader failed reading zip archive: failed finding central directory

[glenn-jocher]

@1ZZZyyyh the error indicates an issue with your dataset path or structure. Ensure the data argument points to a valid dataset configuration file (.yaml) or a properly structured classification dataset with train/, test/, and optionally val/ directories, as outlined in the classification dataset structure guide. Let me know if you need further assistance!