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SqueezeNet was originally described in SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. [1] AlexNet is a deep neural network that has 240 MB of parameters, and SqueezeNet has just 5 MB of parameters.
The dataset has rigorously considered 4 environment factors under different scenes, including illumination, occlusion, object pixel size and clutter, and defines the difficulty levels of each factor explicitly. Classes labelled, training/validation/testing set splits created by benchmark scripts. 1,106,424 RBG-D images images (.png and .pkl)
The datasets are classified, based on the licenses, as Open data and Non-Open data. The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are ...
Oversquashing refers to the bottleneck that is created by squeezing long-range dependencies into fixed-size representations. Countermeasures such as skip connections [ 10 ] [ 38 ] (as in residual neural networks ), gated update rules [ 39 ] and jumping knowledge [ 40 ] can mitigate oversmoothing.
Previously, NIST released two datasets: Special Database 1 (NIST Test Data I, or SD-1); and Special Database 3 (or SD-2). They were released on two CD-ROMs. They were released on two CD-ROMs. SD-1 was the test set, and it contained digits written by high school students, 58,646 images written by 500 different writers.
Can use Theano, Tensorflow or PlaidML as backends Yes No Yes Yes [20] Yes Yes No [21] Yes [22] Yes MATLAB + Deep Learning Toolbox (formally Neural Network Toolbox) MathWorks: 1992 Proprietary: No Linux, macOS, Windows: C, C++, Java, MATLAB: MATLAB: No No Train with Parallel Computing Toolbox and generate CUDA code with GPU Coder [23] No Yes [24 ...
List of datasets in computer vision and image processing ... Julia, [52] MATLAB, [53 ... Google used TensorFlow to create DermAssist, a free mobile application that ...
The Pooling layer [5] is used to reduce the size of data input. The Recurrent layer is used for text processing with a memory function. Similar to the Convolutional layer, the output of recurrent layers are usually fed into a fully-connected layer for further processing. See also: RNN model. [6] [7] [8]