Performance Optimization Strategies for Massive Data Annotation
Recommendations when applying Annot8 to process very large datasets:
- Intelligent batch loading: Split the dataset by 300-500 sheets per batch, keeping the memory footprint <4GB
- Resource mobilization techniques: Assign high CPU priority to Annot8 in the Activity Monitor and turn off the Spotlight indexing service
- Hardware Adaptation Program: External eGPU boosts 4K image rendering speed, SSD storage reduces load latency
- Automated pre-processing: First use ImageMagick to batch resize images to a uniform resolution (1080p recommended)
Specific operations:
- Create a file tree to organize data by category/batch
- Use macOS's purge command to periodically clear the memory cache
- Enable performance mode for the app (turn off animation effects)
- Consider using specialized equipment such as Mac Studio
This answer comes from the articleAnnot8: Quickly Labeling Images to Train AI ModelsThe
May not be reproduced without permission:AI productivity tools " How to overcome performance bottlenecks when labeling large-scale image datasets?
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