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  1. This demo trains a Convolutional Neural Network on the CIFAR-10 dataset in your browser, with nothing but Javascript.[1]
  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.[2]
  3. CIFAR-10 is a set of images that can be used to teach a computer how to recognize objects.[2]
  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.[2]
  5. CIFAR-10 is a labeled subset of the 80 million tiny images dataset.[2]
  6. CIFAR-10 and CIFAR-100 datasets are hosted on University of toronto site at .[3]
  7. The CIFAR-10 dataset consists of 60,000 32 x 32 colour images in 10 classes, with 6,000 images per class.[4]
  8. That’s a key reason why I recommend CIFAR-10 as a good dataset to practice your hyperparameter tuning skills for CNNs.[4]
  9. Here’s how you can build a decent (around 78-80% on validation) CNN model for CIFAR-10.[4]
  10. Once you have mastered CIFAR-10, there’s also CIFAR-100 available in Keras that you can use for further practice.[4]
  11. The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class.[5]
  12. You will first need to download and convert the data format from the CIFAR-10 website.[6]
  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.[6]
  14. Because CIFAR-10 has to measure loss over 10 classes, tf.nn.softmax_cross_entropy_with_logis function is used.[7]
  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.[7]
  16. CIFAR-10 is a more complicated frame-based image dataset than MNIST while it has fewer categories than the Caltech101.[8]
  17. Current state-of-the-art classification accuracy for frame-based algorithms on CIFAR-10 is 96.53% (Springenberg et al., 2015).[8]
  18. CIFAR-10” using a DVS camera (Lichtsteiner et al., 2008).[8]
  19. The CIFAR-10 dataset consists of 60,000 32 × 32 color images in 10 classes, with 6,000 images per class.[8]
  20. We performed an experiment on the CIFAR-10 dataset in Section 13.1.[9]
  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.[9]
  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.[9]
  23. The CIFAR-10 image classification challenge uses 10 categories.[9]
  24. CIFAR-10 is an established computer-vision dataset used for object recognition.[10]
  25. The CIFAR-10 data consists of 60,000 (32×32) color images in 10 classes, with 6000 images per class.[10]
  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.[10]
  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).[11]
  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.[11]
  29. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data).[11]
  30. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art.[11]
  31. CIFAR-10 is a well-understood dataset and widely used for benchmarking computer vision algorithms in the field of machine learning.[12]
  32. # example of loading the cifar10 dataset from matplotlib import pyplot from keras .[12]
  33. datasets import cifar10 from keras .[12]




Spacy 패턴 목록

  • [{'LEMMA': 'CIFAR-10'}]
  • [{'LEMMA': 'cifar10'}]