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    MMClassification

    MMClassification

    OpenMMLab Image Classification Toolbox and Benchmark

    MMClassification is an open-source image classification toolbox based on PyTorch. It is a part of the OpenMMLab project. Supports DenseNet, VAN and PoolFormer, and provide pre-trained models. Supports training on IPU. Supports a series of CSP networks, such as CSP-ResNet, CSP-ResNeXt and CSP-DarkNet. MMClassification is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedback. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to re-implement existing methods and develop their own new classifiers. ...
    Downloads: 0 This Week
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  • 2
    YOLOv4

    YOLOv4

    PyTorch implementation of YOLOv4

    ...It is useful for developers who prefer the PyTorch ecosystem for experimentation, debugging, and integration with other machine learning tooling. The project also connects to the broader YOLOv4 family, including CSP-based architecture ideas and real-time detection improvements. It is best suited for researchers and engineers who want YOLOv4 behavior in a Python-first deep learning environment.
    Downloads: 0 This Week
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  • 3
    YOLOv4-large

    YOLOv4-large

    Scaled-YOLOv4: Scaling Cross Stage Partial Network

    ...The repository includes multiple model variants such as YOLOv4-tiny, YOLOv4-CSP, and large-scale configurations designed for high-performance detection tasks.
    Downloads: 0 This Week
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  • 4
    pySPACE

    pySPACE

    Signal Processing and Classification Environment in Python using YAML

    ...Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text- configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface. ...
    Downloads: 0 This Week
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