Battle of the Backbones
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A large-scale benchmarking framework that compares vision backbones across a diverse suite of computer vision tasks.
Broad benchmarking: Compares CNN and ViT backbones across classification, segmentation, detection, retrieval, and other tasks at matched compute.
Pretraining recipes matter: Shows that pretraining scheme (supervised, self-supervised, language-image) often matters more than the architecture family.
ViT ≠ universal winner: Vision transformers are not universally superior - strong CNN backbones remain competitive or better on several downstream tasks.
Practitioner guide: Functions as a decision reference - the report explicitly maps from task characteristics to recommended backbone + pretraining combinations.
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