Search Results for "dataset" - Page 4
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Showing 134 open source projects for "dataset"
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1
Supervised Reptile
Code for the paper "On First-Order Meta-Learning Algorithms"
...Because Reptile is a first-order algorithm, it avoids computing second derivatives or full meta-gradients, making it computationally simpler while retaining good performance. The repo includes training scripts, dataset fetchers (Omniglot, Mini-ImageNet), and modules for defining the Reptile update logic, variables, and hyperparameters. -
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China Area Data
China's provincial and municipal data
...Versioned datasets preserve older administrative structures so applications can account for regional code changes over time. The latest included major dataset is based primarily on National Bureau of Statistics information from 2019 and also includes Taiwan, Hong Kong, and Macau entries. -
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Tensor2Tensor
Library of deep learning models and datasets
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and... -
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Kimera-VIO
Visual Inertial Odometry with SLAM capabilities and 3D Mesh generation
Visual Inertial Odometry with SLAM capabilities and 3D Mesh generation. Kimera-VIO is a Visual Inertial Odometry pipeline for accurate State Estimation from Stereo + IMU data. It can optionally use Mono + IMU data instead of stereo cameras. -
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PyTorch Natural Language Processing
Basic Utilities for PyTorch Natural Language Processing (NLP)
...It’s built with the very latest research in mind, and was designed from day one to support rapid prototyping. PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders. It’s open-source software, released under the BSD3 license. With your batch in hand, you can use PyTorch to develop and train your model using gradient descent. For example, check out this example code for training on the Stanford Natural Language Inference (SNLI) Corpus. ... -
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Replica Dataset
High-fidelity indoor 3D dataset for AI simulation and robotics
Replica Dataset is a high-quality 3D dataset of realistic indoor environments designed to advance research in computer vision, robotics, and embodied AI. Developed by Facebook Research (now Meta AI), it features accurate geometric reconstructions, high-resolution and high dynamic range textures, and comprehensive semantic annotations. Each environment contains detailed models of real-world spaces, including rooms, furniture, glass, and mirror surfaces. -
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transformer
A TensorFlow Implementation of the Transformer
...It adds components such as byte-pair encoding and shared weight matrices. Training and evaluation are demonstrated with the IWSLT 2016 German-to-English translation dataset. The repository includes preprocessing, training, inference, evaluation, configurable hyperparameters, pretrained checkpoints, and BLEU-based translation results. -
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nlp_chinese_corpus
Large Scale Chinese Corpus for NLP
...The repository gathers several major datasets, including Chinese Wikipedia entries, news articles, encyclopedia-style question answering data, community question answering data, and Chinese-English translation sentence pairs. Each dataset includes descriptions, download links, structure notes, and examples to help users understand how the data is formatted. The corpora can support tasks such as language model pretraining, word vector training, question answering, title generation, keyword generation, translation, and sentence representation learning. Overall, it is a practical resource hub for building or testing Chinese NLP models with larger and more varied datasets. -
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I3D models trained on Kinetics
Convolutional neural network model for video classification
Kinetics-I3D, developed by Google DeepMind, provides trained models and implementation code for the Inflated 3D ConvNet (I3D) architecture introduced in the paper “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset” (CVPR 2017). The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with both RGB and optical flow input streams. The models have achieved state-of-the-art results on benchmark datasets such as UCF101 and HMDB51, and also won first place in the CVPR 2017 Charades Challenge. ... -
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SFBusinessData
Components for Delphi for the reusable organization of data logic
...With SFBusinessData, business logic is stored in specialized data classes that ideally correspond to the structure of the data model. The basis of these classes is the corresponding base class of SFBusinessData, which is ultimately a dataset. The derived, own classes can be instantiated after registration (also via the designer) and used like a dataset. For use through the designer, a wrapper component is available that dynamically creates the specialized data objects at run time. For ease of construction and programmatic manipulation of SQL queries SFBusinessData also provides a SQL generator, which is directly available through the data classes, but can also be used alone. ... -
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RobotsDisallowed
A curated list of the most common and most interesting robots.txt
...The project aggregates domains, notes the targeted bots or user agents, and surfaces patterns for researchers, policymakers, and tool builders. It serves both as a transparency effort and as a resource for people designing allow/deny strategies for automated access. The dataset invites community contributions to keep the picture current as new bots emerge and policies shift. It also highlights the intersection of web standards, ethics, and AI governance by showing how site owners operationalize consent and restriction at scale. -
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benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different... -
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Facets
Visualizations for machine learning datasets
...Understanding your data is critical to building a powerful machine learning system. Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets. Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive. Explore Facets Overview and Facets Dive on the UCI Census Income dataset, used for predicting whether an individual’s income exceeds $50K/yr based on their census data. The census data contains features such as age, education level, and occupation for each individual. ... -
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Active Learning
Framework and examples for active learning with machine learning model
...The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks. -
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Yolo_mark
GUI for marking bounded boxes of objects in images
...It can also extract every specified frame interval from a video before manual labeling begins. OpenCV support, sample configuration files, object tracking, coordinate display, and training instructions make it a compact workflow for legacy YOLO dataset creation. -
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Omniglot
Omniglot data set for one-shot learning
This repository hosts the Omniglot dataset for one-shot learning, containing handwritten characters across multiple alphabets along with stroke data. It includes both MATLAB and Python starter scripts (e.g. demo.m, demo.py) to illustrate how to load the images and stroke sequences and run baseline experiments (such as classification by modified Hausdorff distance). The dataset provides both an image representation of each character and the time-ordered stroke coordinates ([x, y, t]) for each instance. ... -
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Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
...By interpolating both data and labels, the model learns smoother decision boundaries and becomes more robust to noise and adversarial examples. This repository implements mixup for the CIFAR-10 dataset, showcasing its effectiveness in improving generalization, stability, and calibration of neural networks. The approach acts as a regularizer, encouraging linear behavior in the feature space between samples, which helps reduce overfitting and enhance performance on unseen data. -
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Charting tools, backtesting tools, and data visualization tools for the most popular cryptocurrencies.
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Zhao
A compilation of "The Princely Party Relationship Network"
...Because it aggregates content associated with Zhao, the repository functions as a personal or shared knowledge base for readers who want insight into a body of research rather than a traditional software library. Depending on the specific subfolders, it could offer implementations of algorithms, dataset processing utilities, or notebooks that illustrate concepts. Users interested in reading academic work or numerical demonstrations can use the content to deepen understanding of advanced topics. -
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FlowLayout
Android streaming layout, supports single selection
FlowLayout is an Android UI library that implements a “flow” or “tag cloud” layout where items automatically wrap onto new lines as needed, making it ideal for chips, product tags, and selectable labels. Instead of manually placing views, you feed data through an adapter-style API, so tags can be created dynamically from a list and refreshed when the dataset changes. The library supports selection behavior out of the box, including single-select and multi-select modes, so it can behave like a group of checkable chips without you building the state machinery from scratch. It also provides click and selection listeners that let you react when a user taps a tag or when the selected set changes, which is useful for filters and preference UIs. ... -
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Deeplearning-papernotes
Summaries and notes on Deep Learning research papers
...It includes data preprocessing helpers, training scripts, and configuration options so developers can experiment with different filter sizes, dropout rates, and hyperparameters to optimize performance for their dataset. -
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ADO.Net DAC for Delphi
Fastest and most reliable database connectivity solutions for any DB
ADO.Net Data Access Components (DAC) for Delphi is the fastest and most reliable database connectivity solutions for any database using ADO.Net technology in Delphi. It provides data access to data sources such as SQL Server, Oracle and to data sources exposed through OLE DB and ODBC using ADO.Net data provider framework. ADO.Net DAC for Delphi is an enterprise solution with highest performance and unlimited possibilities. Data-sharing consumer applications can use ADO.NET DAC for Delphi to... -
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Solar Calendar is a package for Delphi developers and specially for Persian developers. The package has got set of components for showing and selecting Persian/Iranian/Solar/Shamsi/Jalali date in Persian/Shamsi and Christian(gregorian) interface and many more functions. Follow me: https://www.linkedin.com/in/mohamadkhorsandi
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venn.js
Area proportional Venn and Euler diagrams in JavaScript
...Rendering integrates with D3, allowing developers to manipulate the resulting SVG elements with familiar D3 techniques. Colors, opacity, text, and other visual properties can be customized after rendering. Diagrams can also react dynamically when their underlying dataset changes, making the library useful for interactive data visualization applications. -
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BRTracer
Fault localization using segmentation and stack-trace analysis
...Our empirical results indicate that BRTracer is able to significantly outperform BugLocator on all the tree software projects (i.e., Eclipse, AspectJ, SWT) used in our empirical evaluation. BRTracer is written in Java. We provide a runalbe jar package and its dataset. Note that jdk 1.7 or newer is required to run BRTracer.
