3 Repos
Awesome GitHub RepositoriesConvolutional Layer Conversion
Methods for replacing fully connected layers with convolutional layers.
Distinguishing note: No existing candidates; focuses on architectural flexibility.
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Awesome Convolutional Layer Conversion GitHub Repositories
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- d2l-ai/d2l-en
d2l-ai/d2l-en
29,001Auf GitHub ansehenThis project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex
Uses global average pooling instead of dense layers to reduce parameter counts while maintaining classification performance.
Pythonbookcomputer-visiondata-science - cs231n/cs231n.github.io
cs231n/cs231n.github.io
10,923Auf GitHub ansehenThis project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum
Explains how to convert fully connected layers into convolutional layers to enable spatial evaluation of images.
Jupyter Notebook - kaiminghe/deep-residual-networks
KaimingHe/deep-residual-networks
6,738Auf GitHub ansehenDieses Projekt bietet ein Framework für Deep Residual Networks und vortrainierte PyTorch-Modelle, die für hochpräzise Bilderkennung konzipiert sind. Es implementiert eine Architektur für neuronale Netze, die Skip-Connections nutzt, um das Training sehr tiefer Modelle ohne Gradienten-Degradation zu ermöglichen. Das System ist für Computer-Vision-Aufgaben wie Bildklassifizierung, Objekterkennung und visuelle Datensegmentierung ausgelegt. Es enthält auf ImageNet trainierte Gewichte, um Transfer Learning und das Fine-Tuning von Modellen auf benutzerdefinierten Bilddatensätzen zu unterstützen. Das architektonische Design konzentriert sich auf Residual-Learning-Blöcke, Bottleneck-Layer-Konfigurationen und Batch-Normalisierung, um die Stabilität während des Trainings zu wahren. Das Framework verwendet zudem Global Average Pooling, um Parameter zu reduzieren und Overfitting zu verhindern.
Utilizes global average pooling to replace fully connected layers, reducing total parameters and preventing overfitting.
