CIFAR-10
노트
위키데이터
- ID : Q45037095
말뭉치
- This demo trains a Convolutional Neural Network on the CIFAR-10 dataset in your browser, with nothing but Javascript.[1]
- 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]
- CIFAR-10 is a set of images that can be used to teach a computer how to recognize objects.[2]
- 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]
- CIFAR-10 is a labeled subset of the 80 million tiny images dataset.[2]
- CIFAR-10 and CIFAR-100 datasets are hosted on University of toronto site at https://www.cs.toronto.edu/~kriz/cifar.html .[3]
- The CIFAR-10 dataset consists of 60,000 32 x 32 colour images in 10 classes, with 6,000 images per class.[4]
- That’s a key reason why I recommend CIFAR-10 as a good dataset to practice your hyperparameter tuning skills for CNNs.[4]
- Here’s how you can build a decent (around 78-80% on validation) CNN model for CIFAR-10.[4]
- Once you have mastered CIFAR-10, there’s also CIFAR-100 available in Keras that you can use for further practice.[4]
- The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class.[5]
- You will first need to download and convert the data format from the CIFAR-10 website.[6]
- 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]
- Because CIFAR-10 has to measure loss over 10 classes, tf.nn.softmax_cross_entropy_with_logis function is used.[7]
- 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]
- CIFAR-10 is a more complicated frame-based image dataset than MNIST while it has fewer categories than the Caltech101.[8]
- Current state-of-the-art classification accuracy for frame-based algorithms on CIFAR-10 is 96.53% (Springenberg et al., 2015).[8]
- CIFAR-10” using a DVS camera (Lichtsteiner et al., 2008).[8]
- The CIFAR-10 dataset consists of 60,000 32 × 32 color images in 10 classes, with 6,000 images per class.[8]
- We performed an experiment on the CIFAR-10 dataset in Section 13.1.[9]
- 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]
- 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]
- The CIFAR-10 image classification challenge uses 10 categories.[9]
- CIFAR-10 is an established computer-vision dataset used for object recognition.[10]
- The CIFAR-10 data consists of 60,000 (32×32) color images in 10 classes, with 6000 images per class.[10]
- 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]
- 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]
- 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]
- Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data).[11]
- On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art.[11]
- CIFAR-10 is a well-understood dataset and widely used for benchmarking computer vision algorithms in the field of machine learning.[12]
- # example of loading the cifar10 dataset from matplotlib import pyplot from keras .[12]
- datasets import cifar10 from keras .[12]
소스
- ↑ ConvNetJS CIFAR-10 demo
- ↑ 2.0 2.1 2.2 2.3 Wikipedia
- ↑ Download CIFAR-10 dataset
- ↑ 4.0 4.1 4.2 4.3 Image Classification Using CNN
- ↑ TensorFlow Datasets
- ↑ 6.0 6.1 CIFAR-10 tutorial
- ↑ 7.0 7.1 CIFAR-10 Image Classification in TensorFlow
- ↑ 8.0 8.1 8.2 8.3 CIFAR10-DVS: An Event-Stream Dataset for Object Classification
- ↑ 9.0 9.1 9.2 9.3 13.13. Image Classification (CIFAR-10) on Kaggle — Dive into Deep Learning 0.15.1 documentation
- ↑ 10.0 10.1 10.2 Achieving 90% accuracy in Object Recognition Task on CIFAR-10 Dataset with Keras: Convolutional Neural Networks
- ↑ 11.0 11.1 11.2 11.3 CIFAR-10 on Benchmarks.AI
- ↑ 12.0 12.1 12.2 How to Develop a CNN From Scratch for CIFAR-10 Photo Classification