Is 80% accuracy on CIFAR10 data with CNN normal?

Is 80% accuracy on CIFAR10 data with CNN normal? - Reddit

I got 80% accuracy on the data. Is this normal for CIFAR10? I want to understand if it is possible to build a better network or is the just not going to happen.

CIFAR10 CNN Model 85.97 Accuracy - Kaggle

If you look at the output below, the model is beginning to achieve 80% accuracy on the validation set around 30-35 epochs, and the convergence after that is ...

python - CNN train accuracy gets better during training, but test ...

If you do data normalization like that, then your network is fine: it hits ~65-70% test accuracy after 5 epochs, which is a good result. Note ...

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

The problem is “solved.” It is relatively straightforward to achieve 80% classification accuracy. Top performance on the problem is achieved ...

How much accuracy can a 18000 parameters convolutional neural ...

However, with a well-designed CNN architecture and appropriate training, it is possible to achieve reasonably high accuracy on the CIFAR-10 ...

CIFAR-10 Image Classification: Linear Model vs CNN - Medium

The objective is to achieve a good macro accuracy (at least 80%) using only 20 epochs. ... A common method for preprocessing image data is to ...

How to increase accuracy of All-CNN C on CIFAR-10 test set [closed]

I am trying to implement the paper Striving for Simplicity specifically the model All-CNN C on CIFAR-10 without data augmentation. This model is ...

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

The current state-of-the-art on CIFAR-10 is efficient adaptive ensembling. See a full comparison of 242 papers with code.

What percentage has been reached on CIFAR 10 using only a multi ...

However, with a well-designed CNN architecture and appropriate training, it is possible to achieve reasonably high accuracy on the CIFAR-10 ...

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

However, while getting 90% accuracy on MNIST is trivial, getting 90% on Cifar10 requires serious work. In this tutorial, the mission is to reach ...

CIFAR10-CNN – Weights & Biases - Wandb

CIFAR10 - CNN · Abstract · Preprocessing · VGG-like Model · Log-sum-exp trick · experiment · Best Accuracy: 88.15% · Conclusion.

Achieving 90% accuracy in Object Recognition Task on CIFAR-10 ...

As in my previous post "Setting up Deep Learning in Windows : Installing Keras with Tensorflow-GPU", I ran cifar-10.py, an object ...

CIFAR-10 classification using Keras Tutorial - Ermlab Software

As we can see in the chart below, the best accuracy for 4-layer CNN is for epochs between 20-50. For 6-layer CNN is for epochs between 10-20 ...

Need help for improving accuracy on cifar 10 datasets - Fast.ai Forums

Hey!I have actually created a model on cifar 10 datasets using resnet34 model.But getting around 81 percent of accuracy ,it is done by ...

Classifying images from CIFAR-10 with a convolutional neural ...

You might be wondering why you can't get an accuracy any higher. First things first, 50% isn't bad for a simple CNN. Pure guessing would get you 10% accuracy.

HOW FAR CAN WE GO WITHOUT CONVOLUTION: IM - OpenReview

layers, albeit not as pronounced as on the CIFAR-10 data. ... By comparison, a typical convolutional network yielding an accuracy higher than 80% on CIFAR10.

94% on CIFAR-10 in 3.29 Seconds on a Single GPU - arXiv

Timing begins when the method is first given access to training data, and ends when it produces test-set predictions. The method is considered ...

Image Classification Using CNN - Analytics Vidhya

... 80% on validation) CNN model for CIFAR-10. Notice how the shape ... Data Exploration| Big Data| Common Machine Learning Algorithms| Machine ...

Classification datasets results - Rodrigo Benenson

Reaches 85.02% without data augmentation. With data augmented with horizontal reﬂections and translations, 90.5% accuracy on test set is achieved. 89.67%, APAC: ...

CIFAR-10 Image Classification in TensorFlow - Towards Data Science

Since this project is going to use CNN for the classification tasks, the original row vector is not appropriate. In order to feed an image data ...