Train subclass in Coco data set about ultralytics HOT 4 OPEN

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pip install ultralytics

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from ultralytics.

@dat-nguyenvn hi Dat,

Thank you for your question! Let's address your queries step-by-step:

1. Re-training 3 subclasses in COCO dataset using pre-trained weights on your data:

   To re-train a model on specific classes from the COCO dataset using pre-trained weights, you can filter the classes during training. However, the classes argument is not directly applicable in the train method. Instead, you should create a custom dataset configuration file that includes only the classes you are interested in. Here's how you can do it:

   1. Create a custom dataset YAML file:

      # custom_coco.yaml
      path: ../datasets/coco  # path to your dataset
      train: images/train2017  # train images (relative to 'path')
      val: images/val2017  # val images (relative to 'path')
      test:  # test images (optional)
      
      nc: 3  # number of classes
      names: ['zebra', 'elephant', 'giraffe']  # class names

   2. Filter your dataset:
      You will need to filter the COCO dataset to include only the images containing your classes of interest. This can be done using a script to process the COCO annotations and create a new dataset with only the specified classes.

   3. Train the model:

      from ultralytics import YOLO
      
      # Load a pre-trained model
      model = YOLO('yolov8n.pt')
      
      # Train the model on your custom dataset
      results = model.train(data='custom_coco.yaml', epochs=100, imgsz=640)

2. Using the classes argument in training:
   The classes argument is used for filtering predictions during inference, not for training. For training, you should use a custom dataset configuration file as shown above.

3. Downloading and splitting the COCO dataset:
   If you prefer, you can download the entire COCO dataset, filter out the images containing only the classes of interest, and then mix them with your custom data. This approach ensures that you have a balanced dataset for training.

   Here's a brief outline of the steps:

   • Download the COCO dataset.
   • Filter the dataset to include only images with zebra, elephant, and giraffe.
   • Combine this filtered dataset with your custom data.
   • Create a custom dataset YAML file as shown above.
   • Train the model using the combined dataset.

I hope this helps! If you encounter any issues or need further assistance, feel free to ask. Happy training! 🚀

from ultralytics.

Thank for your feed back!
Do you have any quick tips/code to download and filter CoCo dataset as you mention?
Dat

from ultralytics.