Is ConvNet Making a Comeback? Unraveling Their Performance on Web-Scale Datasets and Matching Vision Transformers

Researchers have challenged the prevailing perception within the subject of laptop imaginative and prescient that Imaginative and prescient Transformers (ViTs) outperform Convolutional Neural Networks (ConvNets) when given entry to massive web-scale datasets. They introduce a ConvNet structure known as NFNet, which is pre-trained on an enormous dataset known as JFT-4B, containing roughly 4 billion labeled pictures from 30,000 courses. Their intention is to guage the scaling properties of NFNet fashions and decide how they carry out compared to ViTs with related computational budgets.

In recent times, ViTs have gained recognition, and there’s a widespread perception that they surpass ConvNets in efficiency, particularly when coping with massive datasets. Nevertheless, this perception lacks substantial proof, as most research have in contrast ViTs to weak ConvNet baselines. Moreover, ViTs have been pre-trained with considerably bigger computational budgets, elevating questions in regards to the precise efficiency variations between these architectures.

ConvNets, particularly ResNets, have been the go-to alternative for laptop imaginative and prescient duties for years. Nonetheless, the rise of ViTs, that are Transformer-based fashions, has led to a shift in the best way efficiency is evaluated, with a deal with fashions pre-trained on massive, web-scale datasets.

Researchers introduce NFNet, a ConvNet structure, and pre-train it on the huge JFT-4B dataset, adhering to the structure and coaching process with out important modifications. They look at how the efficiency of NFNet scales with various computational budgets, starting from 0.4k to 110k TPU-v4 core compute hours. Their purpose is to find out if NFNet can match ViTs when it comes to efficiency with related computational assets.

The analysis staff trains completely different NFNet fashions with various depths and widths on the JFT-4B dataset. They fine-tune these pre-trained fashions on ImageNet and plot their efficiency towards the compute funds used throughout pre-training. In addition they observe a log-log scaling regulation, discovering that bigger computational budgets result in higher efficiency. Curiously, they discover that the optimum mannequin dimension and epoch funds improve in tandem.

The analysis staff finds that their costliest pre-trained NFNet mannequin, an NFNet-F7+, achieves an ImageNet High-1 accuracy of 90.3% with 110k TPU-v4 core hours for pre-training and 1.6k TPU-v4 core hours for fine-tuning. Moreover, by introducing repeated augmentation throughout fine-tuning, they obtain a outstanding 90.4% High-1 accuracy. Comparatively, ViT fashions, which frequently require extra substantial pre-training budgets, obtain related efficiency.

In conclusion, this analysis challenges the prevailing perception that ViTs considerably outperform ConvNets when educated with related computational budgets. They exhibit that NFNet fashions can obtain aggressive outcomes on ImageNet, matching the efficiency of ViTs. The research emphasizes that compute and knowledge availability are vital elements in mannequin efficiency. Whereas ViTs have their deserves, ConvNets like NFNet stay formidable contenders, particularly when educated at a big scale. This work encourages a good and balanced analysis of various architectures, contemplating each their efficiency and computational necessities.

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