How can I train a model to detect graphic such as L-band and Aston

Project type : Graphic Detection
OS :Linux

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I want to detect a L-band which is graphic fired during cricket matches it is in L shape so I have created 4-5 different model versions for detecting L-band but still there is an error in detecting L-band the model is not able to detect L-band properly it is detecting every vertical line as L-band so how can i train my model for L-band that it will not detect every vertical line as L-band
And same with Aston it not in vertical shape it is fired horizontally so how can i train my model that it will detect my both Aston and L-band while perfectly differentiating between them and the screen

Based on the information provided in the extracts, here are a few suggestions that might help improve your model’s performance:

  1. Data Annotation: Ensure that your images are correctly annotated. The quality of your annotations can significantly impact your model’s performance. Roboflow provides a guide on how to create “ground truth” annotations that lead to better “model predictions” here.

  2. Data Augmentation: You can use data augmentation techniques to create a more diverse dataset. This can help your model generalize better and reduce overfitting. You can learn more about data augmentation in computer vision here.

  3. Model Training: Make sure you’re using the right model for your task and that your training parameters (like learning rate, batch size, etc.) are set appropriately. Roboflow provides a guide on training your first model here.

  4. Model Testing: Test your model with unseen data to assess its performance. You can do this using the Roboflow Deploy page as explained here.

If you’re still facing issues, it might be helpful to reach out to the Roboflow community on their forum for more specific advice.

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