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  2. SqueezeNet - Wikipedia

    en.wikipedia.org/wiki/SqueezeNet

    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.

  3. Vision transformer - Wikipedia

    en.wikipedia.org/wiki/Vision_transformer

    Image Classification, Object Detection, Video Deepfake Detection, [41] Image segmentation, [42] Anomaly detection, Image Synthesis, Cluster analysis, Autonomous Driving. [ 6 ] [ 7 ] ViT had been used for image generation as backbones for GAN [ 43 ] and for diffusion models (diffusion transformer, or DiT).

  4. Medical open network for AI - Wikipedia

    en.wikipedia.org/wiki/Medical_open_network_for_AI

    MONAI Core image segmentation example. Pipeline from training data retrieval through model implementation, training, and optimization to model inference. Within MONAI Core, researchers can find a collection of tools and functionalities for dataset processing, loading, Deep learning (DL) model implementation, and evaluation. These utilities ...

  5. U-Net - Wikipedia

    en.wikipedia.org/wiki/U-Net

    Segmentation of a 512 × 512 image takes less than a second on a modern (2015) GPU using the U-Net architecture. [1] [3] [4] [5] The U-Net architecture has also been employed in diffusion models for iterative image denoising. [6] This technology underlies many modern image generation models, such as DALL-E, Midjourney, and Stable Diffusion.

  6. Caffe (software) - Wikipedia

    en.wikipedia.org/wiki/Caffe_(software)

    Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation. It supports CNN, RCNN, LSTM and fully-connected neural network designs. [8] Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL. [9] [10]

  7. LOBPCG - Wikipedia

    en.wikipedia.org/wiki/LOBPCG

    Image segmentation via spectral graph partitioning by LOBPCG with multigrid preconditioning has been first proposed in [53] and actually tested in [54] and. [55] The latter approach has been later implemented in Python scikit-learn [56] that uses LOBPCG from SciPy with algebraic multigrid preconditioning for solving the eigenvalue problem for ...

  8. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    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. 100x100 pixels, white background. 90483 Images (jpg)

  9. Region Based Convolutional Neural Networks - Wikipedia

    en.wikipedia.org/wiki/Region_Based_Convolutional...

    Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision, and specifically object detection and localization. [1] The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output, where each bounding box contains an object and also the category (e.g. car or ...