of the total training volume, containing diverse synsets from the original hierarchy. We propose a "Shard-First" training protocol:
This paper explores the efficacy of using compressed data shards, specifically the 090101.7z subset, to achieve rapid model convergence in high-resolution image classification. We investigate whether a strategically sampled shard can serve as a high-fidelity proxy for the full ImageNet-1K dataset, reducing computational overhead during the initial architectural search phase. 090101.7z
Our preliminary benchmarks suggest that the 090101.7z shard maintains enough semantic diversity to reach 60% of top-1 accuracy within only 10% of the total training time, making it an ideal candidate for "Sanity-Check" runs in resource-constrained environments. of the total training volume, containing diverse synsets
Fine-tuning the proxy-trained weights on the full dataset to measure "warm-start" acceleration. of the total training volume