Advice needed?
📰 Reddit r/deeplearning
Learn to build a shoe pairing solution using deep learning and computer vision to achieve 100% recall, tolerating false positives, and understand the importance of data preprocessing and model selection
Action Steps
- Build a dataset of shoe images with top-down views
- Apply REMBG (silueta) to clean up the background of images
- Embed the cleaned images using a deep learning model like tf_efficientnetv2_s.in21k_ft_in1k
- Configure the model to prioritize recall over precision
- Test the model on a subset of the dataset to evaluate its performance
- Run the model on the entire dataset to find pairs of shoes
Who Needs to Know This
Data scientists and software engineers on a team can benefit from this solution to improve the efficiency of shoe pairing, and product managers can use this to inform business decisions
Key Insight
💡 Prioritizing recall over precision is crucial when false positives can be tolerated, and data preprocessing with REMBG can significantly improve model performance
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👟 Building a shoe pairing solution with 100% recall using deep learning and computer vision! #AI #ComputerVision
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