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Tools and configurations for extracting multi-scale feature maps from neural network backbones.
Distinguishing note: None of the candidates were provided; this captures the specific architectural requirement of multi-scale output extraction.
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This project is a comprehensive library of state-of-the-art neural network architectures designed for image classification and feature extraction. It provides a complete deep learning training framework that supports distributed execution, allowing users to build, train, and fine-tune vision models using optimized schedulers and pre-configured training recipes. The library distinguishes itself through a modular backbone architecture that treats neural networks as decoupled feature extractors, enabling the retrieval of multi-scale outputs for downstream tasks like object detection and segmenta
Hierarchical feature extraction configures backbone networks to output multi-scale feature maps at specified indices for downstream tasks like object detection.