transfer

[giantonti1801]

Search before asking

• [X] I have searched the HUB issues and discussions and found no similar questions.

Question

I am training in ultralitycs a model for the detection of a specific object and I would like this object for which I am training it to be added to the yolov8n classes: example currently yolov8n has 75 classes and I want to train it to detect another object without losing the previous ones, at the end of the training you should have 76 classes

Additional

No response

[glenn-jocher]

@giantonti1801 hello,

Thank you for reaching out! To achieve your goal of adding a new class to the existing YOLOv8 model without losing the previous ones, you can follow these steps:

1. Modify the Dataset Configuration: Update your dataset configuration file to include the new class. If the original model has 75 classes, you should add your new class as the 76th entry.

2. Update the Model Configuration: Ensure that the model configuration reflects the new number of classes. For example, if you are using the yolov8n.yaml file, update the nc (number of classes) parameter to 76.

3. Transfer Learning: Use transfer learning to fine-tune the model with your new dataset. This way, the model retains the knowledge of the original 75 classes while learning to detect the new class.

Here is a sample code snippet to illustrate the process:

from ultralytics import YOLO

# Load the pre-trained YOLOv8 model
model = YOLO('yolov8n.pt')

# Update the number of classes in the model configuration
model.model.nc = 76

# Train the model with your dataset
model.train(data='path/to/your/dataset.yaml', epochs=50)

Make sure your dataset.yaml file includes all 76 classes. Here is an example of how the dataset.yaml might look:

train: path/to/train/images
val: path/to/val/images

nc: 76
names:
  0: class1
  1: class2
  ...
  75: new_class

By following these steps, you should be able to train your model to detect the new object while retaining the ability to detect the original 75 classes.

If you encounter any issues or have further questions, please ensure you are using the latest version of the Ultralytics packages and feel free to reach out again. Happy training! 😊

[giantonti1801]

Hola

¡Gracias por contactarnos! Para lograr su objetivo de agregar una nueva clase al modelo YOLOv8 existente sin perder las anteriores, puede seguir estos pasos:

1. Modificar la configuración del conjunto de datos: actualice el archivo de configuración del conjunto de datos para incluir la nueva clase. Si el modelo original tiene 75 clases, debe agregar su nueva clase como la entrada número 76.
2. Actualizar la configuración del modelo: asegúrese de que la configuración del modelo refleje el nuevo número de clases. Por ejemplo, si está utilizando el archivo, actualice el parámetro (número de clases) a 76.yolov8n.yaml``nc
3. Transferencia de aprendizaje: utilice la transferencia de aprendizaje para ajustar el modelo con su nuevo conjunto de datos. De esta manera, el modelo conserva el conocimiento de las 75 clases originales mientras aprende a detectar la nueva clase.

A continuación, se muestra un fragmento de código de muestra para ilustrar el proceso:

from ultralytics import YOLO

# Load the pre-trained YOLOv8 model
model = YOLO('yolov8n.pt')

# Update the number of classes in the model configuration
model.model.nc = 76

# Train the model with your dataset
model.train(data='path/to/your/dataset.yaml', epochs=50)

Asegúrese de que su archivo incluya las 76 clases. A continuación, se muestra un ejemplo de cómo podría verse:dataset.yaml``dataset.yaml

train: path/to/train/images
val: path/to/val/images

nc: 76
names:
  0: class1
  1: class2
  ...
  75: new_class

Siguiendo estos pasos, debería poder entrenar el modelo para detectar el nuevo objeto y, al mismo tiempo, conservar la capacidad de detectar las 75 clases originales.

Si tiene algún problema o tiene más preguntas, asegúrese de que está utilizando la última versión de los paquetes de Ultralytics y no dude en ponerse en contacto con nosotros de nuevo. ¡Feliz entrenamiento! 😊

@giantonti1801 hello,

Thank you for reaching out! To achieve your goal of adding a new class to the existing YOLOv8 model without losing the previous ones, you can follow these steps:

1. Modify the Dataset Configuration: Update your dataset configuration file to include the new class. If the original model has 75 classes, you should add your new class as the 76th entry.
2. Update the Model Configuration: Ensure that the model configuration reflects the new number of classes. For example, if you are using the yolov8n.yaml file, update the nc (number of classes) parameter to 76.
3. Transfer Learning: Use transfer learning to fine-tune the model with your new dataset. This way, the model retains the knowledge of the original 75 classes while learning to detect the new class.

Here is a sample code snippet to illustrate the process:

from ultralytics import YOLO

# Load the pre-trained YOLOv8 model
model = YOLO('yolov8n.pt')

# Update the number of classes in the model configuration
model.model.nc = 76

# Train the model with your dataset
model.train(data='path/to/your/dataset.yaml', epochs=50)

Make sure your dataset.yaml file includes all 76 classes. Here is an example of how the dataset.yaml might look:

train: path/to/train/images
val: path/to/val/images

nc: 76
names:
  0: class1
  1: class2
  ...
  75: new_class

By following these steps, you should be able to train your model to detect the new object while retaining the ability to detect the original 75 classes.

If you encounter any issues or have further questions, please ensure you are using the latest version of the Ultralytics packages and feel free to reach out again. Happy training! 😊

ok, assuming that I have a data set with a single class_id '0', I must initially change '0' to '76' and then in the yaml file I put nc = '76' but where I have doubts about name I put it alone watch or should I place all the yolov8n classes even if there is only one class in the dataset?

[glenn-jocher]

Hello @giantonti1801,

Great question! When you have a dataset with a single class and you want to add it as the 76th class to an existing YOLOv8 model, you should indeed update the class_id in your annotations to 75 (since class IDs are zero-indexed). However, in your dataset.yaml file, you need to list all 76 class names, even if your dataset only contains one class.

Here’s how you can do it:

1. Update Annotations: Change the class_id in your annotations from 0 to 75.

2. Update dataset.yaml: Ensure that the dataset.yaml file includes all 76 class names. You can copy the names of the existing 75 classes and add your new class as the 76th entry.

Here is an example of how your dataset.yaml file should look:

train: path/to/train/images
val: path/to/val/images

nc: 76
names:
  0: class1
  1: class2
  ...
  74: class75
  75: new_class

In this example, new_class is the name of your new class. Even if your dataset only contains this new class, you need to include all the previous class names in the names list.

3. Train the Model: Use the updated dataset.yaml file to train your model.

from ultralytics import YOLO

# Load the pre-trained YOLOv8 model
model = YOLO('yolov8n.pt')

# Update the number of classes in the model configuration
model.model.nc = 76

# Train the model with your dataset
model.train(data='path/to/your/dataset.yaml', epochs=50)

By following these steps, your model will be able to detect the new class while retaining the ability to detect the original 75 classes.

If you have any further questions or run into any issues, please ensure you are using the latest version of the Ultralytics packages and feel free to reach out again. Happy training! 😊