090101.7z

Fine-tuning the proxy-trained weights on the full dataset to measure "warm-start" acceleration.

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. 090101.7z

Measuring the latency of extracting .7z archives versus standard .tar or raw image folders. Fine-tuning the proxy-trained weights on the full dataset

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. This paper explores the efficacy of using compressed

of the total training volume, containing diverse synsets from the original hierarchy. We propose a "Shard-First" training protocol:

Training a ResNet-50 and a Swin-Transformer solely on the data within 090101.7z .