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Here are the classes in the dataset, as well as 10 random images from each: airplane automobile bird cat deer dog frog horse ship truck The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort. "Truck" includes only big trucks. Neither includes pickup trucks. Download If you're going to use this dataset, please cite the tech report at the bottom of this page. Version Size md5sum CIFAR-10 python version 163 MB c58f30108f718f92721af3b95e74349a CIFAR-10 Matlab version 175 MB 70270af85842c9e89bb428ec9976c926 CIFAR-10 binary version (suitable for C programs) 162 MB c32a1d4ab5d03f1284b67883e8d87530 Baseline results You can find some baseline replicable results on this dataset on the project page for cuda-convnet. These results were obtained with a convolutional neural network. Briefly, they are 18% test error without data augmentation and 11% with. Additionally, Jasper Snoek has a new paper in which he used Bayesian hyperparameter optimization to find nice settings of the weight decay and other hyperparameters, which allowed him to obtain a test error rate of 15% without data augmentation) using the architecture of the net that got 18. Other results Rodrigo Benenson has been kind enough to collect results on CIFAR-10/100 and other datasets on his website; click here to view. Dataset layout Python / Matlab versions I will describe the layout of the Python version of the dataset. The layout of the Matlab version is identical. The archive contains the files data_batch_1, data_batch_2. data_batch_5, as well as test_batch. Each of these files is a Python "pickled" object produced with cPickle. Here is a python2 routine which will open such a file and return a dictionary: def unpickle(file) import cPickle with open(file, rb' as fo: dict. fo) return dict And a python3 version: import pickle dict. fo, encoding= bytes' Loaded in this way, each of the batch files contains a dictionary with the following elements: data. a 10000x3072 numpy array of uint8 s. Each row of the array stores a 32x32 colour image. The first 1024 entries contain the red channel values, the next 1024 the green, and the final 1024 the blue. The image is stored in row-major order, so that the first 32 entries of the array are the red channel values of the first row of the image. labels. a list of 10000 numbers in the range 0-9. The number at index i indicates the label of the i th image in the array data. The dataset contains another file, called. It too contains a Python dictionary object. It has the following entries: label_names. a 10-element list which gives meaningful names to the numeric labels in the labels array described above. For example, label_names[0. airplane" label_names[1. automobile" etc. Binary version The binary version contains the files. as well as. Each of these files is formatted as follows: <1 x label><3072 x pixel. <1 x label><3072 x pixel> In other words, the first byte is the label of the first image, which is a number in the range 0-9. The next 3072 bytes are the values of the pixels of the image. The first 1024 bytes are the red channel values, the next 1024 the green, and the final 1024 the blue. The values are stored in row-major order, so the first 32 bytes are the red channel values of the first row of the image. Each file contains 10000 such 3073-byte "rows" of images, although there is nothing delimiting the rows. Therefore each file should be exactly 30730000 bytes long. There is another file, called. This is an ASCII file that maps numeric labels in the range 0-9 to meaningful class names. It is merely a list of the 10 class names, one per row. The class name on row i corresponds to numeric label i. The CIFAR-100 dataset This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs. Here is the list of classes in the CIFAR-100: Superclass Classes aquatic mammals beaver, dolphin, otter, seal, whale fish aquarium fish, flatfish, ray, shark, trout flowers orchids, poppies, roses, sunflowers, tulips food containers bottles, bowls, cans, cups, plates fruit and vegetables apples, mushrooms, oranges, pears, sweet peppers household electrical devices clock, computer keyboard, lamp, telephone, television household furniture bed, chair, couch, table, wardrobe insects bee, beetle, butterfly, caterpillar, cockroach large carnivores bear, leopard, lion, tiger, wolf large man-made outdoor things bridge, castle, house, road, skyscraper large natural outdoor scenes cloud, forest, mountain, plain, sea large omnivores and herbivores camel, cattle, chimpanzee, elephant, kangaroo medium-sized mammals fox, porcupine, possum, raccoon, skunk non-insect invertebrates crab, lobster, snail, spider, worm people baby, boy, girl, man, woman reptiles crocodile, dinosaur, lizard, snake, turtle small mammals hamster, mouse, rabbit, shrew, squirrel trees maple, oak, palm, pine, willow vehicles 1 bicycle, bus, motorcycle, pickup truck, train vehicles 2 lawn-mower, rocket, streetcar, tank, tractor Yes, I know mushrooms aren't really fruit or vegetables and bears aren't really carnivores. CIFAR-100 python version 161 MB eb9058c3a382ffc7106e4002c42a8d85 CIFAR-100 Matlab version 6a4bfa1dcd5c9453dda6bb54194911f4 CIFAR-100 binary version (suitable for C programs) 03b5dce01913d631647c71ecec9e9cb8 The python and Matlab versions are identical in layout to the CIFAR-10, so I won't waste space describing them here. The binary version of the CIFAR-100 is just like the binary version of the CIFAR-10, except that each image has two label bytes (coarse and fine) and 3072 pixel bytes, so the binary files look like this: <1 x coarse label><1 x fine label><3072 x pixel. <1 x coarse label><1 x fine label><3072 x pixel> Indices into the original 80 million tiny images dataset Sivan Sabato was kind enough to provide this file, which maps CIFAR-100 images to images in the 80 million tiny images dataset. Sivan Writes: The file has 60000 rows, each row contains a single index into the tiny db, where the first image in the tiny db is indexed "1. 0" stands for an image that is not from the tiny db. The first 50000 lines correspond to the training set, and the last 10000 lines correspond to the test set. Reference This tech report (Chapter 3) describes the dataset and the methodology followed when collecting it in much greater detail. Please cite it if you intend to use this dataset. Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009.

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X All Systems Operational Normally (233) IMDb 6. 2 132 min 2019 Zhi You Yun Zhi Dao is a movie starring Xuan Huang, Caiyu Yang, and Lydia Peckham. The film revolves around a Chinese man who returns to New Zealand following the death of his wife and begins to discover that she harbored a number... Starring Fan Xu, Xuan Huang, Caiyu Yang, Lydia Peckham Genres Romance, Drama Director Xiaogang Feng Watch for 0. 00 with Prime Disclaimer: This site does not store any files. Download romance «Zhi You Yun Zhi Dao» Customers who watched also watched.

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