[fix] Prevent Out-of-Memory Errors #180
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The primary issue is the inefficiency of repeatedly loading a large model into memory for each new instance of the `MMDetection` class. This process occurred every time an image required labeling, which led to rapidly exhausting available memory resources. Solution: The solution introduced a class-level attribute within the `MMDetection` class to store and share a single model instance across all instances of the class, ensuring the model loads only once. This approach dramatically reduced memory consumption and prevented out-of-memory errors by eliminating redundant model loading and leveraging model instance reuse.
JimmyMa99
approved these changes
Aug 21, 2024
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The primary issue is the inefficiency of repeatedly loading a large model into memory for each new instance of the
MMDetectionclass. This process occurred every time an image required labeling, which led to rapidly exhausting available memory resources.Solution:
The solution introduced a class-level attribute within the
MMDetectionclass to store and share a single model instance across all instances of the class, ensuring the model loads only once. This approach dramatically reduced memory consumption and prevented out-of-memory errors by eliminating redundant model loading and leveraging model instance reuse.