4 Repos
Awesome GitHub RepositoriesStochastic Depth Regularization
Regularization techniques that randomly drop network paths during training to prevent overfitting.
Distinct from Overfitting Reduction Techniques: Specifically implements stochastic depth as a path-dropping regularization, distinct from general data-driven overfitting reduction.
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Awesome Stochastic Depth Regularization GitHub Repositories
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- facebookresearch/convnext
facebookresearch/ConvNeXt
6,388Auf GitHub ansehenCode release for ConvNeXt model
Applies stochastic depth regularization by randomly dropping residual blocks during training.
Python - mosaicml/composer
mosaicml/composer
5,485Auf GitHub ansehenComposer ist ein Framework für verteiltes Training mit PyTorch, das für die Skalierung großer Modelle über Multi-Node-GPU-Cluster hinweg entwickelt wurde. Es fungiert als Trainer für Large Language Models, als verteilter Modelloptimierer und als Manager für den Trainingslebenszyklus. Das Projekt hebt sich als Bibliothek für Deep-Learning-Regularisierung hervor und bietet spezialisierte Optimierungstechniken wie Sharpness Aware Minimization, MixUp und CutMix, um die Generalisierung von Modellen zu verbessern. Es differenziert seinen Trainingsablauf zudem durch den Einsatz von Sequence Length Warmup, progressivem Layer-Freezing und Sharded-State-Checkpointing für die Wiederherstellung großer Modelle. Das Framework deckt ein breites Spektrum an Funktionen ab, darunter die Orchestrierung von verteiltem Training, das Management von Mixed-Precision-Hardware und Cloud-natives Daten-Streaming. Es bietet zudem umfangreiche Monitoring- und Observability-Tools für die Diagnose von GPU-Speicher, die Erkennung von Trainingsdivergenz und die Verfolgung des Durchsatzes. Das Projekt enthält einen CLI-Launcher zur Automatisierung der Ausführung von Multi-GPU-Trainingsjobs über mehrere Nodes hinweg.
Provides stochastic depth regularization to randomly drop network paths during training and prevent overfitting in deep models.
Python - ai-dawang/plugnplay-modules
ai-dawang/PlugNPlay-Modules
4,968Auf GitHub ansehenPlugNPlay-Modules is a collection of reusable PyTorch computer vision modules and deep learning architectural components. It provides a library of standardized building blocks for constructing neural networks, focusing on attention mechanisms, signal processing layers, and feature fusion modules. The project is distinguished by its extensive variety of attention primitives, covering spatial, channel, and temporal weighting, as well as specialized variants like deformable, frequency-enhanced, and linear-complexity attention. It also implements advanced signal processing tools within the neural
Implements stochastic depth to randomly drop sample paths during training to improve model generalization.
Python - lightly-ai/lightly
lightly-ai/lightly
3,684Auf GitHub ansehenLightly is a self-supervised learning framework and computer vision data curation tool designed to manage large image datasets and train models on unlabeled data. It functions as a PyTorch vision library and dataset management SDK, providing tools to convert raw images into high-dimensional vectors for similarity search, visualization, and feature extraction. The project implements a variety of self-supervised architectures, including MoCo, SimCLR, VICReg, Barlow Twins, and masked image modeling. It distinguishes itself by combining these learning frameworks with active learning capabilities,
Injects dropout into transformer blocks via stochastic depth regularization to prevent overfitting during training.
Pythoncomputer-visioncontrastive-learningcontributions-welcome
