CIFAR-10

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위키데이터

• ID : Q45037095

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1. This demo trains a Convolutional Neural Network on the CIFAR-10 dataset in your browser, with nothing but Javascript.
2. The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms.
3. CIFAR-10 is a set of images that can be used to teach a computer how to recognize objects.
4. Since the images in CIFAR-10 are low-resolution (32x32), this dataset can allow researchers to quickly try different algorithms to see what works.
5. CIFAR-10 is a labeled subset of the 80 million tiny images dataset.
6. CIFAR-10 and CIFAR-100 datasets are hosted on University of toronto site at https://www.cs.toronto.edu/~kriz/cifar.html .
7. The CIFAR-10 dataset consists of 60,000 32 x 32 colour images in 10 classes, with 6,000 images per class.
8. That’s a key reason why I recommend CIFAR-10 as a good dataset to practice your hyperparameter tuning skills for CNNs.
9. Here’s how you can build a decent (around 78-80% on validation) CNN model for CIFAR-10.
10. Once you have mastered CIFAR-10, there’s also CIFAR-100 available in Keras that you can use for further practice.
11. The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class.
12. You will first need to download and convert the data format from the CIFAR-10 website.
13. The CIFAR-10 model is a CNN that composes layers of convolution, pooling, rectified linear unit (ReLU) nonlinearities, and local contrast normalization with a linear classifier on top of it all.
14. Because CIFAR-10 has to measure loss over 10 classes, tf.nn.softmax_cross_entropy_with_logis function is used.
15. Because CIFAR-10 dataset comes with 5 separate batches, and each batch contains different image data, train_neural_network should be run over every batches.
16. CIFAR-10 is a more complicated frame-based image dataset than MNIST while it has fewer categories than the Caltech101.
17. Current state-of-the-art classification accuracy for frame-based algorithms on CIFAR-10 is 96.53% (Springenberg et al., 2015).
18. CIFAR-10” using a DVS camera (Lichtsteiner et al., 2008).
19. The CIFAR-10 dataset consists of 60,000 32 × 32 color images in 10 classes, with 6,000 images per class.
20. We performed an experiment on the CIFAR-10 dataset in Section 13.1.
21. Now, we will apply the knowledge we learned in the previous sections in order to participate in the Kaggle competition, which addresses CIFAR-10 image classification problems.
22. Downloading the Dataset¶ After logging in to Kaggle, we can click on the “Data” tab on the CIFAR-10 image classification competition webpage shown in Fig.
23. The CIFAR-10 image classification challenge uses 10 categories.
24. CIFAR-10 is an established computer-vision dataset used for object recognition.
25. The CIFAR-10 data consists of 60,000 (32×32) color images in 10 classes, with 6000 images per class.
26. We will call the def show_imgs(X) method defined in first section “CIFAR-10 task – Object Recognition in Images” to display 16 images in 4*4 grid.
27. 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).
28. 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.
29. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data).
30. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art.
31. CIFAR-10 is a well-understood dataset and widely used for benchmarking computer vision algorithms in the field of machine learning.
32. # example of loading the cifar10 dataset from matplotlib import pyplot from keras .
33. datasets import cifar10 from keras .