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This task involves implementing the training for the stop sign detection model. The script should handle dataset loading, training, and evaluation using a structured train-validation-test split. (we need to wait on dataset to get the "negative" part of the dataset.
Key performance metrics like accuracy, precision, recall, and mAP must be tracked during training. The script should include checkpointing to save the best model and prevent overfitting. After training, the model should be saved and versioned properly for easy deployment.
This task involves implementing the training for the stop sign detection model. The script should handle dataset loading, training, and evaluation using a structured train-validation-test split. (we need to wait on dataset to get the "negative" part of the dataset.
Key performance metrics like accuracy, precision, recall, and mAP must be tracked during training. The script should include checkpointing to save the best model and prevent overfitting. After training, the model should be saved and versioned properly for easy deployment.
This website will give an understanding what the metrics mean: https://www.v7labs.com/blog/mean-average-precision
This trained model will be working alongside line detection and confidence thresholding for accurate stop sign recognition.
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