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Showing 1010 open source projects for "repository"
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Amazon Braket Ocean Plugin
A Python plugin for using Ocean with Amazon Braket
...Make sure that your AWS account is onboarded to Amazon Braket. The Amazon Braket Ocean Plugin can be installed with pip. You can also install from source by cloning this repository and running a pip install command in the root directory of the repository. This package provides samplers which use Braket solvers. These samplers extend abstract base classes provided in Ocean's dimod and thus have the same interfaces as other samplers in Ocean. -
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ConvNeXt
Code release for ConvNeXt model
...ConvNeXt’s clean, hierarchical structure makes it efficient for both pretraining and fine-tuning across a wide range of visual recognition tasks. It achieves competitive or superior results on ImageNet and downstream datasets while being easier to deploy and train than transformers. The repository provides pretrained models, training recipes, and ablation studies demonstrating how incremental design choices collectively yield state-of-the-art performance. -
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Alphafold2
Unofficial Pytorch implementation / replication of Alphafold2
...Once this is replicated, I intend to fold all available amino acid sequences out there in-silico and release it as an academic torrent, to further science. Deepmind has open sourced the official code in Jax, along with the weights! This repository will now be geared towards a straight pytorch translation with some improvements on positional encoding. lhatsk has reported training a modified trunk of this repository, using the same setup as trRosetta, with competitive results. The underlying assumption is that the trunk works on the residue level, and then constitutes to atomic level for the structure module, whether it be SE3 Transformers, E(n)-Transformer, or EGNN doing the refinement. -
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Brain Tokyo Workshop
Experiments and code from Google Brain’s Tokyo research workshop
The repository includes implementations, experimental data, and supporting research papers that accompany published studies. Notable works such as Weight Agnostic Neural Networks and Neuroevolution of Self-Interpretable Agents highlight the team’s exploration of how AI can learn more efficiently and transparently. Overall, this repository serves as an open research hub for sharing ideas and advancing the understanding of intelligent systems. -
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Crunch PNG
Insane(ly slow but wicked good) PNG image optimization
...This approach leads to a significant file size gain relative to lossless approaches at the expense of a relatively modest decrease in image quality. Continuous benchmark testing is available in our GitHub Actions CI. Please see the benchmarks directory of this repository for details about the benchmarking approach and instructions on how to execute benchmarks locally on the reference images distributed in this repository or with your own image files. -
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Official YOLOv7
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art
YOLOv7 is the official implementation of the paper “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.” It is a PyTorch-based object detection project focused on high speed and strong accuracy for real-time computer vision. The repository provides model definitions, training scripts, testing tools, inference examples, pretrained weights, and deployment-oriented materials. YOLOv7 introduced training-time improvements that raise accuracy without increasing inference cost, which is why the project became important in real-time detection research. It supports multiple model sizes and related tasks such as object detection and instance segmentation through associated branches or weights. ... -
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Blank Grabber
The most powerful stealer written in Python 3
...The safest way to describe it is as a malware sample for defensive analysis, detection engineering, and awareness of common stealer techniques. It is no longer actively maintained in the original repository. -
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LaMDA-pytorch
Open-source pre-training implementation of Google's LaMDA in PyTorch
Open-source pre-training implementation of Google's LaMDA research paper in PyTorch. The totally not sentient AI. This repository will cover the 2B parameter implementation of the pre-training architecture as that is likely what most can afford to train. You can review Google's latest blog post from 2022 which details LaMDA here. You can also view their previous blog post from 2021 on the model. -
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Machine Learning Glossary
Machine learning glossary
Machine Learning Glossary is an open educational project that provides clear explanations of machine learning terminology and concepts through visual diagrams and concise definitions. The goal of the repository is to make machine learning topics easier to understand by presenting definitions alongside examples, visual illustrations, and references for further learning. It covers a wide range of topics including neural networks, regression models, optimization techniques, loss functions, and evaluation metrics. The content is organized into sections that progressively introduce key ideas from basic machine learning concepts to more advanced mathematical topics. ... -
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RoBERTa for Chinese
RoBERTa Chinese pre-training model: RoBERTa for Chinese
RoBERTa for Chinese is a Chinese RoBERTa pretrained model repository for language understanding tasks. It provides TensorFlow and PyTorch-compatible model releases trained on large-scale Chinese text. The project follows the main RoBERTa training ideas, including removing next sentence prediction, using more diverse data, training longer, increasing batch size, and tuning optimization settings. -
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WaveRNN
WaveRNN Vocoder + TTS
...As a vocoder, WaveRNN models raw audio with a compact recurrent neural network that can generate high-quality waveforms more efficiently than many traditional autoregressive models. The repository includes scripts and code for preprocessing datasets such as LJSpeech, training Tacotron to produce mel spectrograms, training WaveRNN on those spectrograms (with optional GTA data), and finally generating audio. A quick_start.py script allows users to immediately synthesize example sentences from a pretrained model and inspect both generated audio and attention plots. ... -
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Talking Head Anime from a Single Image
Demo for the "Talking Head Anime from a Single Image"
...The underlying model uses deep learning techniques to predict how different facial features and body parts should move based on pose parameters or input signals. This allows the software to create realistic animated frames while preserving the identity and appearance of the original character. The repository includes demo applications that allow users to interact with the system through graphical controls or webcam input to drive character motion. These demonstrations illustrate how generative neural rendering can be used to build real-time avatar systems for virtual characters. -
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100+ Python Projects Challenge
100+ Python Projects Challenge
...The repository is aimed at learners who benefit from doing, modifying, and expanding examples. It is best used as a progressive challenge list for turning Python knowledge into visible working projects. -
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Discord ATIO
ATIO is a AIO Script Developed with Python3
Discord-All-Tools-In-One is an archived Python toolkit that collected many Discord-related automation, abuse, and account-manipulation scripts in one interface. The repository itself describes the project as potentially malicious and unethical, and many of its modules involve activities that can violate privacy, platform rules, or the law. It includes tools related to selfbotting, account abuse, token misuse, webhook spam, server disruption, and remote-access malware. Because of that, it should not be treated as a normal Discord utility or legitimate bot framework. ... -
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MuZero General
A commented and documented implementation of MuZero
...MuZero is a model-based reinforcement learning method that combines neural networks with Monte Carlo Tree Search to learn decision-making policies without requiring explicit knowledge of the environment’s dynamics. The repository provides a well-documented and commented implementation designed primarily for educational purposes. It allows researchers and developers to train reinforcement learning agents that can learn to play games such as Atari environments or board games. The framework is modular so that users can easily add new environments by defining the game logic and associated hyperparameters. ... -
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Deep learning time series forecasting
Deep learning PyTorch library for time series forecasting
...Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series forecasting framework. Currently, Task-TS from CoronaWhy primarily maintains this repository. Pull requests are welcome. Historically, this repository provided open-source benchmarks and codes for flash flood and river flow forecasting. Full transformer (SimpleTransformer in model_dict): The full original transformer with all 8 encoder and decoder blocks. Requires passing the target in at inference. -
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Google Cloud Vision API examples
Sample code for Google Cloud Vision
The repository demonstrates concrete image understanding use cases, such as landmark detection and mobile photo analysis with label and face detection, so developers can see how Vision API outputs are consumed in real interfaces and workflows. Although the repository has been marked as deprecated in favor of language-specific repositories for new work, it still serves as a broad reference hub for legacy examples and multi-language implementation patterns. -
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Faster-Rcnn
This is a pytorch implementation library of faster-rcnn
...It is designed for training and evaluating detectors on VOC-format datasets, including VOC07+12 and custom datasets arranged with VOC-style annotations and images. The repository includes scripts for training, prediction, evaluation, annotation generation, and model summary inspection. It supports backbone options through pretrained VGG and ResNet weights, making it useful for comparing feature extractors. The project also includes learning rate scheduling through step and cosine methods, optimizer choices between Adam and SGD, adaptive learning rate behavior based on batch size, image cropping, FPS testing, video prediction, and batch prediction. ... -
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DeepLearning Tutorial
Deep Learning Tutorial, Excellent Articles, Deep Learning Tutorial
...The repository organizes these materials into structured tutorials and references that allow readers to explore deep learning concepts progressively. Many of the resources include explanations of common model architectures used in computer vision and artificial intelligence. The project also collects recommended articles and educational documents that expand on deep learning theory and practical implementation strategies. -
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DeepLabv3 Plus
Encoder-Decoder with Atrous Separable Convolution
...It implements the encoder-decoder architecture with atrous separable convolution and provides a practical workflow for training, prediction, and mIoU evaluation. The repository supports VOC-style segmentation datasets and includes utilities for annotation generation, JSON dataset conversion, model summary inspection, prediction, and metric calculation. It provides pretrained weight workflows for MobileNetV2 and Xception backbones and notes that the correct backbone should be selected during training and prediction. ... -
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Unet
Source code for unet-pytorch, which can train its own model
Unet-pytorch is a PyTorch implementation of U-Net for semantic segmentation workflows. The repository is built around training, prediction, and mIoU evaluation for VOC-style segmentation data and medical-style datasets. It includes scripts for general training, medical dataset training, prediction, annotation handling, model summaries, and evaluation. The project supports multiple backbones, data processing utilities, extensive comments, and adjustable training parameters. -
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MAE (Masked Autoencoders)
PyTorch implementation of MAE
...After pretraining, the encoder serves as a powerful backbone for downstream tasks like image classification, segmentation, and detection, achieving top performance with minimal fine-tuning. The repository provides pretrained models, fine-tuning scripts, evaluation protocols, and visualization tools for reconstruction quality and learned features. -
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Texar-PyTorch
Integrating the Best of TF into PyTorch, for Machine Learning
...The tool is designed for both researchers and practitioners for fast prototyping and experimentation. Texar-PyTorch was originally developed and is actively contributed by Petuum and CMU in collaboration with other institutes. A mirror of this repository is maintained by Petuum Open Source. Texar-PyTorch integrates many of the best features of TensorFlow into PyTorch, delivering highly usable and customizable modules superior to PyTorch native ones. Texar-PyTorch (this repo) and Texar-TF have mostly the same interfaces. Both further combine the best design of TF and PyTorch. Data processing, model architectures, loss functions, training and inference algorithms, evaluation, etc. -
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RQ-Transformer
Implementation of RQ Transformer, autoregressive image generation
Implementation of RQ Transformer, which proposes a more efficient way of training multi-dimensional sequences autoregressively. This repository will only contain the transformer for now. You can use this vector quantization library for the residual VQ. This type of axial autoregressive transformer should be compatible with memcodes, proposed in NWT. It would likely also work well with multi-headed VQ. I also think there is something deeper going on, and have generalized this to any number of dimensions. ... -
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Ungoogled Chromium Android
Android build for ungoogled-chromium
Ungoogled Chromium Android is the Android platform build configuration and support tooling for Ungoogled Chromium on mobile devices, enabling privacy-minded users and developers to compile a version of the Chromium browser for Android that excludes Google-dependent services, telemetry, and tracking. This repository contains platform-specific patches, build targets, and integration scripts to adapt the upstream Chromium source for Android while eliminating components like Google Play Services hooks, automatic updater mechanisms, and preconfigured search engines that compromise privacy. The goal is to offer an Android browser that feels familiar in capability and rendering fidelity but does not phone home, engage with proprietary APIs, or leak usage data to third-party providers. ...