2 个仓库
Awesome GitHub RepositoriesML Baseline Implementations
Canonical model implementations used to establish performance baselines for machine learning tasks.
Distinct from Reference Implementations: Distinct from general reference implementations by specifically targeting ML model accuracy baselines.
Explore 2 awesome GitHub repositories matching education & learning resources · ML Baseline Implementations. Refine with filters or upvote what's useful.
Awesome ML Baseline Implementations GitHub Repositories
用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
- zalandoresearch/fashion-mnist
zalandoresearch/fashion-mnist
12,754在 GitHub 上查看This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy of machine learning models. It provides a standardized collection of labeled fashion product images and training data formatted to be compatible with the MNIST dataset structure. The dataset consists of fixed-dimension grayscale images and label-based category mappings, stored in a binary format. It includes pre-split training and testing sets and a static distribution to ensure consistent cross-model benchmarking. The repository supports image classification benchmarking and
Provides a reference convolutional neural network implementation to establish accuracy baselines.
Pythonbenchmarkcomputer-visionconvolutional-neural-networks - mdeff/fma
mdeff/fma
2,559在 GitHub 上查看This project is a music information retrieval library and research dataset designed for audio feature extraction and music genre classification. It provides a framework for training and evaluating machine learning models that categorize audio tracks into hierarchical genre structures, supported by a collection of open-licensed MP3 tracks and pre-computed features. The project includes a music metadata API client to fetch structured track, album, and artist information from external data sources. It utilizes these external integrations to map parent-child relationships between genres and organ
Provides canonical model implementations to establish performance baselines for audio-based genre recognition.
Jupyter Notebookdatasetdeep-learningmusic-analysis
