multiclass for 1 object

[itachi176]

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How can I detect multiclass for 1 object. eg: detect face and classifier face with mask and face with glasses?

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[glenn-jocher]

@itachi176 you can detect multiclass for 1 object by training your YOLOv5 model with multiple classes. In your case, you can define three classes: "face", "face with mask", and "face with glasses".

To train the model, you will need to create a custom dataset with images labeled for each class. You can use labeling tools like LabelImg or RectLabel to annotate your images. Each annotation should include the class label and bounding box coordinates.

Once you have your dataset ready, you can follow the training instructions provided in the YOLOv5 documentation. Make sure to update your data.yaml file to include your new classes, and also modify the model architecture and loss function to accommodate multiple classes.

During the training process, the model will learn to distinguish between different classes and detect the presence of each class in an image.

I hope this helps! Let me know if you have any further questions.

[itachi176]

@itachi176 you can detect multiclass for 1 object by training your YOLOv5 model with multiple classes. In your case, you can define three classes: "face", "face with mask", and "face with glasses".

To train the model, you will need to create a custom dataset with images labeled for each class. You can use labeling tools like LabelImg or RectLabel to annotate your images. Each annotation should include the class label and bounding box coordinates.

Once you have your dataset ready, you can follow the training instructions provided in the YOLOv5 documentation. Make sure to update your data.yaml file to include your new classes, and also modify the model architecture and loss function to accommodate multiple classes.

During the training process, the model will learn to distinguish between different classes and detect the presence of each class in an image.

I hope this helps! Let me know if you have any further questions.

thank you for your response. How can I modify the model architecture, can you give me the file contain functions should be modify?

[glenn-jocher]

@itachi176, in order to modify the model architecture in YOLOv5, you will need to make changes to the models/yolo.py file.

Inside the models/yolo.py file, you will find the YOLOv5 class definition. This class represents the YOLOv5 model architecture.

To add support for multiple classes, you will need to make the following changes:

1. Modify the __init__ method:

   • Update the nc parameter to match the number of classes in your dataset. For your case, it should be set to 3 (face, face with mask, and face with glasses).
   • Update the number of default ch channels to account for the additional classes. For example, if ch = [64, 128, 256, 512, 1024], you will need to add 3 more channels to it.

2. Modify the forward method:

   • Update the last layer of the m module to output the desired number of bounding box attributes and scores for the additional classes.
   • Update the number of anchors and anchor strides accordingly.

Please note that making these changes to the model architecture is an advanced task, and you should be familiar with PyTorch and deep learning concepts.

I hope this guidance helps you make the required modifications. Feel free to ask if you have any further questions!

[itachi176]

@itachi176, in order to modify the model architecture in YOLOv5, you will need to make changes to the models/yolo.py file.

Inside the models/yolo.py file, you will find the YOLOv5 class definition. This class represents the YOLOv5 model architecture.

To add support for multiple classes, you will need to make the following changes:

1. Modify the __init__ method:

   • Update the nc parameter to match the number of classes in your dataset. For your case, it should be set to 3 (face, face with mask, and face with glasses).
   • Update the number of default ch channels to account for the additional classes. For example, if ch = [64, 128, 256, 512, 1024], you will need to add 3 more channels to it.

2. Modify the forward method:

   • Update the last layer of the m module to output the desired number of bounding box attributes and scores for the additional classes.
   • Update the number of anchors and anchor strides accordingly.

Please note that making these changes to the model architecture is an advanced task, and you should be familiar with PyTorch and deep learning concepts.

I hope this guidance helps you make the required modifications. Feel free to ask if you have any further questions!

I have a question about custom dataset annotation. I have 2 ideas for my dataset annotation such as: face_label / coordinates_of_face
face_with_mask_label / coordinates_of_face face_with_glasses_label / coordinates_of_face

or

face_label / face_with_mask_label / face_with_glasses_label / coordinates_of_face

which is True format?

[itachi176]

And ouput will be [bounding box face, face_label, confidence_face, mask_label, confidence_mask, glasses_label, confidence_glasses].is this right?

[glenn-jocher]

@itachi176 based on the information you provided, it seems like you are trying to define the annotation format for your custom dataset.

In this case, both annotation formats can work, but the choice depends on your specific requirements and how you plan to use the dataset.

The first format, where each label is associated with its corresponding coordinates, can be suitable if you want to train your model to detect each specific class individually. This format allows you to have separate annotations for each class and their respective bounding box coordinates.

The second format, where all labels are provided together for each image, can be useful if you want to train your model to detect multiple classes simultaneously. In this case, you would have one annotation per image, with all the labels and corresponding coordinates provided together.

Ultimately, the choice depends on your use case and the specific objectives you have for your model. Consider how you want your model to recognize and classify objects and choose the annotation format that aligns with those goals.

I hope this helps you decide on the appropriate annotation format for your dataset. Let me know if you have any further questions!

[itachi176]

And ouput will be [bounding box face, face_label, confidence_face, mask_label, confidence_mask, glasses_label, confidence_glasses].is this right?

@glenn-jocher help me

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[josmyk]

@glenn-jocher How can I combine trained multiple annotated images to get a single model yolov5? Is that possible?

[glenn-jocher]

@josmyk yes, it is possible to combine multiple trained models in YOLOv5 to create a single model. One approach is to merge the weights of the individual models into a single model configuration file.

To do this, you can use the --weights argument in the YOLOv5 train.py script to load the weights from each individual model. For example, you can specify the weights of the first model using --weights weights1.pt and the weights of the second model using --weights weights2.pt.

During training, the script will load the weights from each model and use them to initialize the corresponding layers in the single model. The training process will then continue with the updated weights, allowing the model to learn from the combined knowledge of the individual models.

Keep in mind that when combining models, it's important to consider any differences in the classes, architecture, or loss functions used in each model. These differences may require additional adjustments to ensure the combined model performs optimally.

I hope this helps! Let me know if you have any further questions.

[Ramesh-Prajapat]

@glenn-jocher Hey Glenn Evening from India!

Currently, I'm working on Image processing/annotation, and I Created separate models/weights files. Now, I want to create/build a single model/weights file for all previous model/weights files,

• How can I do this task? Please suggest me some valuable solution

Thanks!

[glenn-jocher]

@Ramesh-Prajapat hello! Thanks for reaching out.

To create a single model/weights file from multiple separate models in YOLOv5, you can follow these steps:

1. Make sure you have the individual model weights files available.

2. Load the weights of each model using the torch.load() function to obtain the saved state dictionaries.

3. Combine the state dictionaries into one dictionary by merging the model parameters. You can use the update() function to merge the parameters.

4. Create a new model instance using model = YourModelClass(...).

5. Load the combined state dictionary into the new model using model.load_state_dict(combined_state_dict).

6. Save the new model's state dictionary using torch.save() to obtain a single model/weights file.

Remember to handle any differences in classes, architecture, or loss functions used in each model as these may require additional adjustments for optimal performance.

I hope this guidance helps! Let me know if you have any further questions.

[lion600]

@josmyk yes, it is possible to combine multiple trained models in YOLOv5 to create a single model. One approach is to merge the weights of the individual models into a single model configuration file.

To do this, you can use the --weights argument in the YOLOv5 train.py script to load the weights from each individual model. For example, you can specify the weights of the first model using --weights weights1.pt and the weights of the second model using --weights weights2.pt.

During training, the script will load the weights from each model and use them to initialize the corresponding layers in the single model. The training process will then continue with the updated weights, allowing the model to learn from the combined knowledge of the individual models.

Keep in mind that when combining models, it's important to consider any differences in the classes, architecture, or loss functions used in each model. These differences may require additional adjustments to ensure the combined model performs optimally.

I hope this helps! Let me know if you have any further questions.

@glenn-jocher I am also working on the same problem. I have two models. one model is detecting jeans and another is detecting shirt and shoe. I edited yaml file like

train:

• jeans/train
• shirt_shoe/train

val:

• jeans/valid
• shirt_shoe/valid test: # test images (optional)

Classes

names: 0: jeans 1: shirt 2: shoe

after that ,I could not able to understand where to add the model (.pt file)

Train YOLOv5s on COCO128 for 3 epochs

!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights /content/best1.pt --weights /content/best2.pt --cache

this was the line for training the model. Can you explain more. It will be really helpful. Thank you.

[glenn-jocher]

@lion600 to combine two trained models that detect different classes, you can follow these steps:

1. Make sure you have the individual model weights files available (best1.pt and best2.pt in your case).

2. Edit your YAML file to include the paths for both classes' training and validation data.

3. Specify the classes and their corresponding indices in the YAML file.

4. In the training script command, use the --weights argument to load the weights from both models. For example:

python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights /content/best1.pt --weights /content/best2.pt --cache

This command loads the weights from best1.pt and best2.pt into the model during training. The script will use these combined weights to initialize the corresponding layers of a single model.

Remember to consider any differences in the classes, architecture, or loss functions used in each model. You may need to make additional adjustments to ensure the combined model performs optimally.

I hope this clarifies the steps for combining your models! Let me know if you have any further questions.

[lion600]

@glenn-jocher Thank you for your response. In third point how to specify the classes like I have name: 0: jeans and another class name: 0: shirt 1: shoe

how to combine them together in yaml for building one single model?

[josmyk]

@josmyk yes, it is possible to combine multiple trained models in YOLOv5 to create a single model. One approach is to merge the weights of the individual models into a single model configuration file.

To do this, you can use the --weights argument in the YOLOv5 train.py script to load the weights from each individual model. For example, you can specify the weights of the first model using --weights weights1.pt and the weights of the second model using --weights weights2.pt.

During training, the script will load the weights from each model and use them to initialize the corresponding layers in the single model. The training process will then continue with the updated weights, allowing the model to learn from the combined knowledge of the individual models.

Keep in mind that when combining models, it's important to consider any differences in the classes, architecture, or loss functions used in each model. These differences may require additional adjustments to ensure the combined model performs optimally.

I hope this helps! Let me know if you have any further questions.

@glenn-jocher Thank you for your response. Can I get a clarity about, specifying path& classes in the YAML file.

I've one model with classes 0: person 1: tie Another model with 0: watch I need to combine these two models to get a single model. How can I specify my classes in YAML file and how to give paths for both classes' training and validation data?

[glenn-jocher]

@josmyk you can specify the classes and their corresponding indices in the YAML file by including them under the names section. In your case, you would specify the classes for your combined model as follows:

names:
  0: person
  1: tie
  2: watch

This assigns the index 2 to the class "watch" from your second model. Ensure that the indices for the classes in your combined model are continuous and not overlapping.

To specify paths for both classes' training and validation data, you can include them under the train and val sections in the YAML file. Here's an example:

train:
  - path/to/person/training/data
  - path/to/tie/training/data
  - path/to/watch/training/data

val:
  - path/to/person/validation/data
  - path/to/tie/validation/data
  - path/to/watch/validation/data

Make sure to replace path/to/ with the actual paths to your training and validation data for each class.

By specifying the classes and their corresponding data paths in the YAML file, you can train your combined model with multiple classes.

I hope this clarifies how to specify classes and paths in the YAML file. Let me know if you have any further questions!

[josmyk]

@josmyk you can specify the classes and their corresponding indices in the YAML file by including them under the names section. In your case, you would specify the classes for your combined model as follows:

names:
  0: person
  1: tie
  2: watch

This assigns the index 2 to the class "watch" from your second model. Ensure that the indices for the classes in your combined model are continuous and not overlapping.

To specify paths for both classes' training and validation data, you can include them under the train and val sections in the YAML file. Here's an example:

train:
  - path/to/person/training/data
  - path/to/tie/training/data
  - path/to/watch/training/data

val:
  - path/to/person/validation/data
  - path/to/tie/validation/data
  - path/to/watch/validation/data

Make sure to replace path/to/ with the actual paths to your training and validation data for each class.

By specifying the classes and their corresponding data paths in the YAML file, you can train your combined model with multiple classes.

I hope this clarifies how to specify classes and paths in the YAML file. Let me know if you have any further questions!

@glenn-jocher Thank you so much.. its working now

[glenn-jocher]

@josmyk great to hear that it's working now! If you have any further questions or need assistance with anything else, feel free to ask. Happy training!