What is the current best performing model on ImageNet?

The current best performing model on ImageNet, a widely recognized benchmark for image classification, is BiT-L (ResNet).

ImageNet Performance Leaders

ImageNet is a crucial dataset for training and evaluating computer vision models, particularly for tasks like image classification. Models are ranked based on their "Top 1 Accuracy," which measures how often the model's highest-probability prediction matches the correct label from a single prediction.

The BiT-L (ResNet) model, standing for Big Transfer - Large, achieves a remarkable 87.54% Top 1 Accuracy. This model is part of the Big Transfer family, developed by Google, which emphasizes the effectiveness of pre-training large models on extensive datasets and then fine-tuning them for various downstream tasks, even with relatively small target datasets. Its key strength lies in its ability to transfer learned knowledge effectively across different tasks and datasets, leading to robust performance.

Top Performing Models on ImageNet

Here's a breakdown of the leading models and their performance on the ImageNet (Image Classification) benchmark:

For more detailed benchmarks and the most recent updates on image classification models, you can refer to the official ImageNet Classification Leaderboard on Papers With Code.