ImageNet-1k Leaderboard

Welcome to the ImageNet-1k Leaderboard! This leaderboard tracks the performance of various computer vision models on the ImageNet-1k dataset, which contains 1.2 million training images across 1000 classes.

Key Metrics

• Top-1 Accuracy: Percentage of images where the model's top prediction is correct
• Top-5 Accuracy: Percentage of images where the correct class is among the top 5 predictions
• Parameters: Number of trainable parameters in the model
• FLOPs: Floating point operations required for inference
• Inference Time: Average time per image (if available)

Dataset

ImageNet-1k is a subset of the ImageNet dataset containing:

• Training set: 1.2M images
• Validation set: 50K images
• Classes: 1000 object categories
• Image size: Variable (typically resized to 224x224 or 384x384)

Hardware Configuration

All results are tested on NVIDIA L4 GPU to ensure consistent and fair comparison across models.

The leaderboard is sorted by Top-1 Accuracy (descending) as the primary metric.

Last updated on Sep 16th 2025