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    MetaCLIP

    MetaCLIP

    ICLR2024 Spotlight: curation/training code, metadata, distribution

    ...The repository provides training logic, adaptation strategies (e.g. prompt tuning, adapter modules), and evaluation across base and target domains to measure how well the model retains its general knowledge while specializing as needed. It includes utilities to fine-tune vision-language embeddings, compute prompt or adapter updates, and benchmark across transfer and retention metrics. MetaCLIP is especially suited for real-world settings where a model must continuously incorporate new visual categories or domains over time.
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  • 2
    Kornia

    Kornia

    Open Source Differentiable Computer Vision Library

    ...It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors. ...
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  • 3
    ConvNet Burden

    ConvNet Burden

    Memory consumption and FLOP count estimates for convnets

    convnet-burden is a MATLAB toolbox / script collection estimating computational cost (FLOPs) and memory consumption of various convolutional neural network architectures. It lets users compute approximate burdens (in FLOPs, memory) for standard image classification CNN models (e.g. ResNet, VGG) based on network definitions. The tool helps researchers compare the computational efficiency of architectures or quantify resource needs. Estimation of memory consumption (e.g. feature map sizes, parameter storage). Support for multiple network definitions/architectures. ...
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  • 4
    tracking.js

    tracking.js

    A modern approach for Computer Vision on the web

    The tracking.js library brings different computer vision algorithms and techniques into the browser environment. By using modern HTML5 specifications, we enable you to do real-time color tracking, face detection and much more, all that with a lightweight core (~7 KB) and intuitive interface. To get started, download the project. This project includes all of the tracking.js examples, source code dependencies you'll need to get started. Unzip the project somewhere on your local drive. The...
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