Search Results for "code blocks sample" - Page 3
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Showing 91 open source projects for "code blocks sample"
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1
Shap-E
Generate 3D objects conditioned on text or images
...Because it works at the level of implicit functions, Shap-E can render output both as textured meshes and NeRF-style volumetric renderings. The repository contains sample notebooks (e.g. sample_text_to_3d.ipynb, sample_image_to_3d.ipynb) so users can try out text → 3D or image → 3D generation. The code is distributed under the MIT license, and includes a “model card” that documents limitations, recommended use, and ethical considerations. -
2
Taylorplot_Neptune
Creation of a Taylorplot for several machine learning models
Here we present the lines of code for creating a taylor plot with python to display several machine learning models. We show the solution for displaying 10 models, but the list and number can be changed simply by modifying the sample list. -
3
Asteroid
The PyTorch-based audio source separation toolkit for researchers
The PyTorch-based audio source separation toolkit for researchers. Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code thats supports a large range of datasets and architectures, and a set of recipes to reproduce some important papers. Building blocks are thought and designed to be seamlessly plugged together. Filterbanks, encoders, maskers, decoders and losses are all common building blocks that can be combined in a flexible way to create new systems. ... -
4
DiT (Diffusion Transformers)
Official PyTorch Implementation of "Scalable Diffusion Models"
DiT (Diffusion Transformer) is a powerful architecture that applies transformer-based modeling directly to diffusion generative processes for high-quality image synthesis. Unlike CNN-based diffusion models, DiT represents the diffusion process in the latent space and processes image tokens through transformer blocks with learned positional encodings, offering scalability and superior sample quality. The model architecture parallels large language models but for image tokens—each block refines noisy latent representations toward cleaner outputs through iterative denoising steps. DiT achieves strong results on benchmarks like ImageNet and LSUN while being architecturally simple and highly modular. ... -
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5
lora-svc
Singing voice change based on whisper, lora for singing voice clone
singing voice change based on whisper, and lora for singing voice clone. You will feel the beauty of the code from this project. Uni-SVC main branch is for singing voice clone based on whisper with speaker encoder and speaker adapter. Uni-SVC main target is to develop lora for SVC. With lora, maybe clone a singer just need 10 stence after 10 minutes train. Each singer is a plug-in of the base model. -
6
Codiga VS Code
VS Code plugin that suggests code blocks as you type and check
VS Code plugin that suggests code blocks as you type and check for errors. Works for JavaScript, TypeScript, Python, Java, Scala, Ruby, PHP, Apex, Docker. -
7
chatgpt HTML
PHP version calls the OpenAI interface for question and answer
The entire network is the most easy to deploy and responds to the fastest ChatGPT environment. The PHP version calls the OpenAI interface for question and answer, uses Stream flow mode communication, and produces while exporting. EventSource is used at the front end to support Markdown format analysis, and formula display, the code is colored. The UI on the page is concise and supports continuous conversations in the context. The source code has only a few files, no frame is used, all PHP... -
8
minGPT
A minimal PyTorch re-implementation of the OpenAI GPT
minGPT is a minimalist, educational re-implementation of the GPT (Generative Pretrained Transformer) architecture built in PyTorch, designed by Andrej Karpathy to expose the core structure of a transformer-based language model in as few lines of code as possible. It strips away extraneous bells and whistles, aiming to show how a sequence of token indices is fed into a stack of transformer blocks and then decoded into the next token probabilities, with both training and inference supported. Because the whole model is around 300 lines of code, users can follow each step—from embedding lookup, positional encodings, multi-head attention, feed-forward layers, to output heads—and thus demystify how GPT-style models work beneath the surface. ... -
9
MTCNN Face Detection Alignment
Joint Face Detection and Alignment
...The algorithm uses a cascade of three convolutional networks (P-Net, R-Net, O-Net) to jointly detect faces (bounding boxes) and align facial landmarks in a coarse-to-fine manner, leveraging multi-task learning. Non-maximum suppression and bounding box regression at each stage. The repository includes Caffe / MATLAB code, support scripts, and instructions for dependencies. Non-maximum suppression and bounding box regression at each stage. Online hard sample mining to improve training robustness. -
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Codeball AI
AI Code Review that finds bugs and fast-tracks your code
Codeball is a code review AI that scores pull requests on a grade from 0 (needs careful review) to 1. Use Codeball to add labels to help you focus, auto-approve PRs, and more. The Codeball action is easy to use (sane defaults) and is highly customizable to fit your workflow when needed. Label PRs when you should review them with caution. Stay sharp, don't let the bugs pass through. -
11
ConvNeXt
Code release for ConvNeXt model
ConvNeXt is a modernized convolutional neural network (CNN) architecture designed to rival Vision Transformers (ViTs) in accuracy and scalability while retaining the simplicity and efficiency of CNNs. It revisits classic ResNet-style backbones through the lens of transformer design trends—large kernel sizes, inverted bottlenecks, layer normalization, and GELU activations—to bridge the performance gap between convolutions and attention-based models. ConvNeXt’s clean, hierarchical structure... -
12
Following Instructions with Feedback
Training Language Models to Follow Instructions with Human Feedback
The following-instructions-human-feedback repository contains the code and supplementary materials underpinning OpenAI’s work in training language models (InstructGPT models) that better follow user instructions through human feedback. The repo hosts the model card, sample automatic evaluation outputs, and labeling guidelines used in the process. It is explicitly tied to the “Training language models to follow instructions with human feedback” paper, and serves as a reference for how OpenAI collects annotation guidelines, runs preference comparisons, and evaluates model behaviors. ... -
13
AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
...You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related example notebooks. These notebooks provide code and descriptions for creating and running workflows in AWS Step Functions Using the AWS Step Functions Data Science SDK. In Amazon SageMaker, example Jupyter notebooks are available in the example notebooks portion of a notebook instance. To run the AWS Step Functions Data Science SDK example notebooks locally, download the sample notebooks and open them in a working Jupyter instance. -
14
AI Platform Training and Prediction
Cloud ML Engine repo
AI Platform Training and Prediction is a collection of machine learning example projects that demonstrate how to train, deploy, and serve models using Google Cloud AI Platform and related services. It includes a wide variety of implementations across frameworks such as TensorFlow, PyTorch, scikit-learn, and XGBoost, allowing developers to explore different approaches to building ML solutions. The repository covers the full machine learning lifecycle, including data preprocessing, model... -
15
Bottender
A framework for building conversational user interfaces
...Design actions for each event and state in your application and Bottender will run accordingly. Bottender lets you create apps on every channel and never compromise on your users’ experience. This approach makes your code more predictable and easier to debug. You can apply progressive enhancement or graceful degradation strategy on your building blocks. There are thousands of bots powered by Bottender. It has been optimized for real-world use cases, automatic batching requests and dozens of other compelling features. With Bottender, you only need a few configurations to make your bot work with channels, automatic server listening, webhook setup, signature verification and so much more. ... -
16
Trax
Deep learning with clear code and speed
Trax is an end-to-end library for deep learning that focuses on clear code and speed. It is actively used and maintained in the Google Brain team. Run a pre-trained Transformer, create a translator in a few lines of code. Features and resources, API docs, where to talk to us, how to open an issue and more. Walkthrough, how Trax works, how to make new models and train on your own data. Trax includes basic models (like ResNet, LSTM, Transformer) and RL algorithms (like REINFORCE, A2C, PPO). It... -
17
CAM
Class Activation Mapping
...The method involves modifying a CNN model slightly (e.g., using global average pooling before the final layer) to produce a weighted combination of feature maps as the class activation map. Integration with existing CNNs (with light modifications). Sample scripts/examples using standard architectures. The repo provides example code and instructions for applying CAM to existing CNN architectures. Visualization of discriminative regions per class. -
18
Machine-Learning
kNN, decision tree, Bayesian, logistic regression, SVM
...Because it's part of the author’s learning-path repositories, it likely is integrated with tutorials, sample datasets, and contextual guidance, which helps users bridge theory. -
19
PyCls
Codebase for Image Classification Research, written in PyTorch
pycls is a focused PyTorch codebase for image classification research that emphasizes reproducibility and strong, transparent baselines. It popularized families like RegNet and supports classic architectures (ResNet, ResNeXt) with clean implementations and consistent training recipes. The repository includes highly tuned schedules, augmentations, and regularization settings that make it straightforward to match reported accuracy without guesswork. Distributed training and mixed precision are... -
20
Image GPT
Large-scale autoregressive pixel model for image generation by OpenAI
...Researchers can use the code to sample new images, evaluate generative loss on datasets like ImageNet or CIFAR-10, and explore the impact of scaling on performance. While the repository is archived and provided as-is, it remains a valuable starting point for experimenting with autoregressive transformers applied directly to raw pixel data. By demonstrating GPT’s flexibility across modalities, Image-GPT influenced subsequent multimodal generative research. -
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wav2letter++
Facebook AI research's automatic speech recognition toolkit
...A sample is specified using 4 columns separated by space (or tabs). -
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ANDTool
Analysis Nuclei DAB (AND) Tool
...The tool requires as input the original RGB images, and the FastRed, FastBlue, DAB channel, easily obtained using the Fiji function: "ImageJ" -> "Image" -> "Colour Deconvolution" -> "FastRed FastBlue DAB" Then, the tool first segment the nuclei using the FastBlue channel and the DAB channel, and then computes statistics by subdividing the sample in three regions according to the FastRed channel: a dark-red ROI, a light-pink ROI and a white ROI. ANDTool is written in MATLAB (The MathWorks, Inc., Massachusetts, USA) and the source code and standalone versions are here available for download. USER MANUAL: see the specific PDF available in the Files section. REQUIREMENTS: MATLAB R2017b and Image Processing Toolbox 10.1 or later versions. ... -
23
Supervised Reptile
Code for the paper "On First-Order Meta-Learning Algorithms"
The supervised-reptile repository contains code associated with the paper “On First-Order Meta-Learning Algorithms”, which introduces Reptile, a meta-learning algorithm for learning model parameter initializations that adapt quickly to new tasks. The implementation here is aimed at supervised few-shot learning settings (e.g. Omniglot, Mini-ImageNet), not reinforcement learning, and includes scripts to run training and evaluation for few-shot classification. -
24
ML.NET Samples
Samples for ML.NET, an open source and cross-platform machine learning
...In addition, it also generates sample C# code to run/score that model plus the C# code that was used to create/train it so you can research what algorithm and settings it is using. -
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PyTracking
Visual tracking library based on PyTorch
A general python framework for visual object tracking and video object segmentation, based on PyTorch. Official implementation of the RTS (ECCV 2022), ToMP (CVPR 2022), KeepTrack (ICCV 2021), LWL (ECCV 2020), KYS (ECCV 2020), PrDiMP (CVPR 2020), DiMP (ICCV 2019), and ATOM (CVPR 2019) trackers, including complete training code and trained models.
