Training a ResNet-50 and a Swin-Transformer solely on the data within 090101.7z .
Training state-of-the-art convolutional neural networks (CNNs) and Vision Transformers (ViTs) requires massive datasets. However, the iterative process of hyperparameter tuning is often bottlenecked by I/O speeds and storage decompression. This study focuses on the 090101.7z archive, evaluating its class distribution and feature variance compared to the complete corpus. 3. Dataset Analysis Source: ImageNet (ILSVRC) training set. Format: Compressed 7z archive to optimize throughput. Scope: Approximately 090101.7z
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
Fine-tuning the proxy-trained weights on the full dataset to measure "warm-start" acceleration. This study focuses on the 090101
Measuring the latency of extracting .7z archives versus standard .tar or raw image folders.