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Images (jpg) Classification 2017–2024 [318] Mihai Oltean Weed-ID.App Database with 1,025 species, 13,500+ images, and 120,000+ characteristics Varying size and background. Labeled by PhD botanist. 13,500 Images, text Classification 1999-2024 [319] Richard Old CottonWeedDet3 Dataset A 3-class weed detection dataset for cotton cropping systems
Images Classification 2013 [47] [48] S. Houben et al. KITTI Vision Benchmark Dataset Autonomous vehicles driving through a mid-size city captured images of various areas using cameras and laser scanners. Many benchmarks extracted from data. >100 GB of data Images, text Classification, object detection 2012 [49] [50] [51] A. Geiger et al. FieldSAFE
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]
[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 ...
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 ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
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.