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Images, Text Classification, Image-Language 2022 [31] SIFT10M Dataset SIFT features of Caltech-256 dataset. Extensive SIFT feature extraction. 11,164,866 Text Classification, object detection 2016 [32] X. Fu et al. LabelMe: Annotated pictures of scenes. Objects outlined. 187,240 Images, text Classification, object detection 2005 [33]
Train/test splits, labeled images, 1360 Images, text Classification 2006 [315] [316] M-E Nilsback et al. Plant Seedlings Dataset 12 category dataset of plant seedlings. Labelled images, segmented images, 5544 Images Classification, detection 2017 [317] Giselsson et al. Fruits-360 Database with images of 131 fruits and vegetables.
A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3]
As the image illustrated below, if only a small portion of the image is shown, it is very difficult to tell what the image is about. Mouth. Even try another portion of the image, it is still difficult to classify the image. Left eye. However, if we increase the contextual of the image, then it makes more sense to recognize. Increased field of ...
Keras: High-level API, providing a wrapper to many other deep learning libraries. Microsoft Cognitive Toolkit; MXNet: an open-source deep learning framework used to train and deploy deep neural networks. PyTorch: Tensors and Dynamic neural networks in Python with GPU acceleration.
[15] [16] MNIST included images only of handwritten digits. EMNIST includes all the images from NIST Special Database 19 (SD 19), which is a large database of 814,255 handwritten uppercase and lower case letters and digits. [17] [18] The images in EMNIST were converted into the same 28x28 pixel format, by the same process, as were the MNIST ...
CIFAR-10 is a labeled subset of the 80 Million Tiny Images dataset from 2008, published in 2009. When the dataset was created, students were paid to label all of the images. [5] Various kinds of convolutional neural networks tend to be the best at recognizing the images in CIFAR-10.