ImageNet Classification Leaderboard
The goal of this page is:
- To keep on track of state-of-the-art (SOTA) on ImageNet Classification and new CNN architectures
- To see the comparison of famous CNN models at a glance (performance, speed, size, etc.)
- To access their research papers and implementations on different frameworks
If you want to keep following this page, please star and watch this repository.
- Mult-Adds: The number of multiply-add operations
- FLOPS: The floating point operations
Numbers in the ‘Reference’ column indicate the reference webpages and papers for each model’s values.
If you want
- To add any value from your own model and paper on the leaderboard
- To revise any mistake in the leaderboard
- To update any value on the existing model
please leave your suggestion in the issue page of this repository.
Byung Soo Ko / email@example.com