The best model for MNIST image classification, based on reported percentage error, is the Branching/Merging CNN + Homogeneous Vector Capsules model.
This sophisticated architecture achieved an exceptionally low error rate of just 0.13% on the MNIST dataset, which is a standard benchmark for evaluating image classification algorithms, particularly for handwritten digit recognition.
Here's a breakdown of the leading models and their performance on the MNIST benchmark:
Rank | Model | Percentage Error |
---|---|---|
1 | Branching/Merging CNN + Homogeneous Vector Capsules | 0.13 |
2 | EnsNet (Ensemble learning in CNN augmented with fully connected subnetworks) | 0.16 |
3 | Efficient-CapsNet | 0.16 |
4 | SOPCNN (Only a single Model) | 0.17 |
These cutting-edge models demonstrate significant advancements in neural network design, often incorporating specialized components like capsule networks or ensemble learning techniques to achieve superior accuracy in image recognition tasks. For the most up-to-date benchmarks and detailed information on these models, comprehensive resources like Papers With Code's MNIST Benchmark are invaluable.