Multi-label classification yolov5

[minhhotboy9x]

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I have a dataset for multi-label classification, how can I set up yolov5-cls training for multi-label?

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[glenn-jocher]

@minhhotboy9x hello! If you have a dataset for multi-label classification and would like to train yolov5-cls for multi-label tasks, you can follow these steps:

1. Make sure your dataset is properly labeled with multiple labels for each image. Each label should be represented by a separate class index.

2. Modify the dataset.yaml file in the data directory to include all the class names and their corresponding indices. Ensure that the nc parameter is set to the total number of classes in your dataset.

3. Update the model configuration file (e.g., yolov5s.yaml, yolov5m.yaml, etc.) to use the classification head instead of the detection head. Set classes to the number of classes in your dataset.

4. Train the model using the train.py script by specifying the appropriate --img size, --batch-size, and --data path to your modified dataset.yaml file. Additionally, set --cfg to the path of your modified model configuration file.

By following these steps, you should be able to train yolov5-cls for multi-label classification tasks.

Let me know if you have any further questions!

[minhhotboy9x]

@glenn-jocher I watched an example on Roboflow and I saw that there is no need for a dataset.yaml, all I need is put images right on their folder labels. However, I wonder if my model will train data as single-label or multi-label. When I read classify/train.py, I saw that criterion model-cls uses is CrossEntropyLoss. Also, I see that there no --cfg in classify/train.py

[glenn-jocher]

@minhhotboy9x, in YOLOv5, the dataset.yaml is an optional file that helps organize and configure your dataset. It can be used to specify the class names and their corresponding indices, as well as other dataset-related settings. However, it is not mandatory.

Regarding your question about training the model for single-label or multi-label classification, the classify/train.py script in YOLOv5 is designed for single-label classification. It uses the CrossEntropyLoss criterion, which assumes that each input has only one correct label.

If you want to train the model for multi-label classification, there is no specific script provided in the YOLOv5 repository. However, you can modify the code to support multi-label classification by using a different loss function, such as BCEWithLogitsLoss or MultiLabelSoftMarginLoss, which are commonly used for multi-label tasks.

As for the --cfg argument, it is used in the detection training process (train.py) to specify the model configuration file (yolov5s.yaml, yolov5m.yaml, etc.). In the classification training process (classify/train.py), the model configuration file is not used, as the architecture for classification (yolov5s, yolov5m, etc.) is fixed within the script.

I hope this clarifies your doubts. Let me know if you have any further questions!

[minhhotboy9x]

@glenn-jocher ok, I get it. Thank you very much

[glenn-jocher]

@minhhotboy9x you're welcome! If you have any more questions or need further assistance, feel free to ask. Happy training with YOLOv5!

[dao027]

@glenn-jocher ok, I get it. Thank you very much

hello, i have the same issue with multi-label classification, have u solved ? can u share your solution?

[glenn-jocher]

@dao027 sure, I'm happy to help. For multi-label classification with YOLOv5, you can modify the code to support multi-label tasks by using a different loss function such as BCEWithLogitsLoss or MultiLabelSoftMarginLoss. These loss functions are commonly used for multi-label tasks and can be implemented in the training script (train.py) to handle multi-label classification.

If you have any more questions or need further assistance, feel free to ask!

[mamdouhhz]

@dao027 sure, I'm happy to help. For multi-label classification with YOLOv5, you can modify the code to support multi-label tasks by using a different loss function such as BCEWithLogitsLoss or MultiLabelSoftMarginLoss. These loss functions are commonly used for multi-label tasks and can be implemented in the training script (train.py) to handle multi-label classification.

If you have any more questions or need further assistance, feel free to ask!

hi, how do i change the yolov5 model to accomodate multi-label classification. i understand i have to change the loss function and the output layer, but how do i do this as ?

[glenn-jocher]

@mamdouhhz hello! To adapt YOLOv5 for multi-label classification, you'll need to modify both the loss function and the model's final layer. Here’s a brief guide:

1. Change the Loss Function: Replace the existing loss function with BCEWithLogitsLoss in your training script. This is suitable for multi-label scenarios.

   import torch.nn as nn
   criterion = nn.BCEWithLogitsLoss()

2. Modify the Output Layer: Adjust the final layer of your model to ensure it uses a sigmoid activation function (handled by BCEWithLogitsLoss) and has the correct number of outputs for your classes. This typically involves modifying the number of output features in the last linear layer to match the number of classes.

   model.fc = nn.Linear(in_features=model.fc.in_features, out_features=num_classes, bias=True)

Make sure to adjust num_classes to the number of labels you have. These changes will allow your model to output a multi-label prediction.

If you need further clarification, feel free to ask! 😊