Need advice for training a YOLOv5-obb model about yolov5 HOT 2 OPEN

@yasmine-lkl hello! 🚀

Great to hear about your project and your proactive steps with YOLOv5-obb for drone-captured image detection. Training models on small objects is a known challenge, but your approach is commendable.

For small datasets like yours (150-200 images), ensuring high-quality annotations is key. The more precise your annotations, especially with oriented bounding boxes (OBB), the better your results will likely be. However, the size of your dataset is indeed small, which can impact the model's effectiveness. To enhance your model's performance, consider:

1. Augmentation: Make full use of data augmentation to artificially expand your dataset. Experiment with different types to see what works best for your specific case.
2. Transfer Learning: Start with a pre-trained model and fine-tune it on your dataset. This can significantly reduce the amount of data required for effective training.
3. Iterative Training: Begin training with what you have and iteratively add more data if possible. This can help you gauge the model's performance and understand if more data is needed.
4. Model Selection: For small objects, you might need to adjust the model's architecture. Consider experimenting with different versions of YOLOv5 (e.g., YOLOv5s, YOLOv5m) to find the best fit.

Remember, the journey to an optimal model often involves a lot of trial and testing. Keep experimenting with different configurations, and don't hesitate to revisit your dataset for possible improvements.

Best of luck with your project! If you have more questions or need further assistance, feel free to ask. 🌟

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@glenn-jocher Thank you so much for taking the time to respond to my message and for your invaluable advice! 🙏

I'm thrilled to hear your suggestions regarding data augmentation. Is it possible to perform data augmentation directly on Roboflow? Additionally, I'm considering using tiling to zoom in on my photos, but I'm concerned about how this might affect the annotations. Do you have any solutions for this issue?

I'm also curious about integrating other methods such as self-supervised learning or cross-validation to optimize my model. Would these approaches be beneficial in my case?

Adding pre-trained data isn't feasible since the objects I aim to detect, such as Velux windows, chimneys, and ventilation systems, aren't found in pre-trained datasets.

Ultimately, my goal is to test the tool on orthophotos and integrate it into AutoCAD software for automatic detection of objects in orthophotos. If you have any further advice or suggestions, I would be incredibly grateful!

Thank you once again for your support and guidance. 🚀

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