Cifar-10 best accuracy
BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10 , and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class.

CIFAR-10 on Benchmarks.AI

BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10 , and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class.

Tutorial 2: 94% accuracy on Cifar10 in 2 minutes | by ...

Cifar10 resembles MNIST — both have 10 classes and tiny images. However, while getting 90% accuracy on MNIST is trivial, getting 90% on Cifar10 requires serious work. In this tutorial, the mission...

ResNet9: train to 94% CIFAR10 accuracy in 100 seconds with ...

Among the impressive entries from top-class research institutes and AI Startups, perhaps the biggest leap was brought by David Page from Myrtle. His ResNet9 achieved 94% accuracy on CIFAR10 in barely 79 seconds, less than half of the time needed by last year's winning entry from FastAI.

94% accuracy on CIFAR-10 in 10 minutes with Amazon ...

The top entry for training time on CIFAR-10 used distributed training on multi-GPU to achieve 94% in a little less than 3 minutes! This was achieved by fast.ai. However, the code provided takes a...

CIFAR-10 Benchmark (Image Classification) | Papers With Code

The current state-of-the-art on CIFAR-10 is EffNet-L2 (SAM). See a full comparison of 124 papers with code.

neural networks - CIFAR-10 Can't get above 60% Accuracy ...

It's worth noting that the architectures that get to best -published accuracy on CIFAR-10 (currently in the 90-96% range) are generally more complicated and take many hours to train on GPU hardware.

GitHub - exelban/tensorflow- cifar - 10 : Cifar-10 CNN ...

Accuracy Best accurancy what I receive was 79.12% on test data set. You must to understand that network cant always learn with the same accuracy . But almost always accuracy more than 78%.

GitHub - adhishthite/ cifar10 -optimizers: Comparison of ...

Comparison of different optimizers on the CIFAR-10 dataset. Choosing the right optimizer while working on a Deep Learning project is very important. In this small project, I illustrate the performance of different optimizers on a basic CNN.

Classification datasets results - Rodrigo Benenson

CIFAR-10 who is the best in CIFAR-10 ? CIFAR-10 49 results collected. Units: accuracy % Classify 32x32 colour images. Result Method Venue Details; 96.53%: Fractional Max-Pooling ... Units: accuracy % Classify 32x32 colour images. Result Method Venue Details; 75.72%: Fast and Accurate Deep Network Learning by Exponential Linear Units ...

How to Develop a CNN From Scratch for CIFAR-10 Photo ...

CIFAR-10 is a well-understood dataset and widely used for benchmarking computer vision algorithms in the field of machine learning. The problem is " solved." It is relatively straightforward to achieve 80% classification accuracy .

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