Classification for Indoor Scenes (e.g., Kitchen, Living Room, Dining Room, Bedroom)

[danglu999]

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Question

I am interested in using YOLO for indoor scene detection, specifically to classify scenes such as kitchens, living rooms, dining rooms, and bedrooms. Currently, YOLO's object detection capabilities are well-known and widely used, but I wonder if it could also support this type of classification task directly or with some modification.

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[glenn-jocher]

Hi there! 😊

Great question! YOLO models, including YOLOv8, are primarily designed for object detection, which means they excel at identifying and locating objects within images. However, for scene classification tasks like distinguishing between kitchens, living rooms, etc., you'd typically look at models focusing on overall image classification.

That said, it's possible to repurpose a YOLO model for classification by adapting its final layers and training it on a dataset of labeled scenes. Yet, if your primary goal is scene classification only, you might find models explicitly designed for image classification more straight to the point, such as ResNet or EfficientNet.

If you still wish to explore YOLO for this task, you could start by training a YOLO model on a dataset of indoor scenes. Each scene category (kitchen, living room, etc.) needs to be a "class," and you'll train the model to detect the entire image as one of these scenes.

Here's a very basic conceptual outline for repurposing YOLO for classification:

# Assume 'model' is your loaded YOLO model and 'data' is your dataset

# Adjust the model's architecture to suit classification.
# This step is conceptual and requires modifying the model's code.
model.adapt_for_classification(num_classes=4)  # For kitchen, living room, dining room, bedroom

# Now, train your model on the indoor scenes dataset.
model.train(data='your_scene_dataset.yaml')

Note that adapting YOLO for classification might not be straightforward and could involve significant modifications to the model. For dedicated scene classification tasks, exploring models that are designed for such purposes might yield better results with less effort. 🚀

Hope this helps, and happy coding!

[danglu999]

Thank you so much for your detailed reply! 😊

I really appreciate your suggestion regarding using models like ResNet or EfficientNet for efficiently distinguishing between different indoor scenes. Your guidance has been very helpful.

Thanks again for your help!

[glenn-jocher]

You're welcome! I'm glad the information was helpful to you. If you have any more questions or need further assistance as you explore different models for your project, feel free to reach out. Happy coding! 😊🚀

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