Search Results for "evolution-x" - Page 2
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Showing 96 open source projects for "evolution-x"
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Norfair
Lightweight Python library for adding real-time multi-object tracking
Norfair is a customizable lightweight Python library for real-time multi-object tracking. Using Norfair, you can add tracking capabilities to any detector with just a few lines of code. Any detector expressing its detections as a series of (x, y) coordinates can be used with Norfair. This includes detectors performing tasks such as object or keypoint detection. It can easily be inserted into complex video processing pipelines to add tracking to existing projects. At the same time, it is possible to build a video inference loop from scratch using just Norfair and a detector. ... -
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The SpeechBrain Toolkit
A PyTorch-based Speech Toolkit
...SpeechBrain supports state-of-the-art methods for end-to-end speech recognition, including models based on CTC, CTC+attention, transducers, transformers, and neural language models relying on recurrent neural networks and transformers. Speaker recognition is already deployed in a wide variety of realistic applications. SpeechBrain provides different models for speaker recognition, including X-vector, ECAPA-TDNN, PLDA, and contrastive learning. Spectral masking, spectral mapping, and time-domain enhancement are different methods already available within SpeechBrain. Separation methods such as Conv-TasNet, DualPath RNN, and SepFormer are implemented as well. SpeechBrain provides efficient and GPU-friendly speech augmentation pipelines and acoustic features extraction. -
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Luna AI
Virtual AI anchor that combines state-of-the-art technology
...The project supports multiple rendering backends for the avatar, such as Live2D, Unreal Engine (UE), and “xuniren,” and can output to streaming platforms like Bilibili, Douyin, Kuaishou, WeChat Channels, Pinduoduo, Douyu, YouTube, Twitch, and TikTok. For voice, it integrates with numerous TTS engines (Edge-TTS, VITS-Fast, ElevenLabs, VALL-E-X, OpenVoice, GPT-SoVITS, Azure TTS, fish-speech, ChatTTS, CosyVoice, F5-TTS, MultiTTS, MeloTTS, and others), and can optionally pass the output through voice conversion systems like so-vits-svc or DDSP-SVC to change timbre. -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
...With only a few lines of code, ktrain allows you to easily and quickly. ktrain purposely pins to a lower version of transformers to include support for older versions of TensorFlow. If you need a newer version of transformers, it is usually safe for you to upgrade transformers, as long as you do it after installing ktrain. As of v0.30.x, TensorFlow installation is optional and only required if training neural networks. -
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TensorFlow Model Garden
Models and examples built with TensorFlow
...To improve the transparency and reproducibility of our models, training logs on TensorBoard.dev are also provided for models to the extent possible though not all models are suitable. A flexible and lightweight library that users can easily use or fork when writing customized training loop code in TensorFlow 2.x. It seamlessly integrates with tf.distribute and supports running on different device types (CPU, GPU, and TPU). -
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Step-Audio-EditX
LLM-based Reinforcement Learning audio edit model
...Because the model is trained with a “large-margin learning” objective over many synthesized and natural speech samples, it gains robust control over expressive attributes, and can perform iterative editing: e.g. you could record a line, then ask the model to “make it sadder,” “speak slower,” or “change accent to X.” -
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CodeGeeX2
CodeGeeX2: A More Powerful Multilingual Code Generation Model
CodeGeeX2 is the second-generation multilingual code generation model from ZhipuAI, built upon the ChatGLM2-6B architecture and trained on 600B code tokens. Compared to the first generation, it delivers a significant boost in programming ability across multiple languages, outperforming even larger models like StarCoder-15B in some benchmarks despite having only 6B parameters. The model excels at code generation, translation, summarization, debugging, and comment generation, and it supports... -
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Vidi2
Large Multimodal Models for Video Understanding and Editing
Vidi is a family of large multimodal models developed for deep video understanding and editing tasks, integrating vision, audio, and language to allow sophisticated querying and manipulation of video content. It’s designed to process long-form, real-world videos and answer complex queries such as “when in this clip does X happen?” or “where in the frame is object Y during that moment?” — offering temporal retrieval, spatio-temporal grounding (i.e. locating objects over time + space), and even video question answering. Vidi targets applications like intelligent video editing, automated video search, content analysis, and editing assistance, enabling users to efficiently locate relevant segments and objects in hours-long footage. ... -
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OpenCV
Open Source Computer Vision Library
The Open Source Computer Vision Library has >2500 algorithms, extensive documentation and sample code for real-time computer vision. It works on Windows, Linux, Mac OS X, Android, iOS in your browser through JavaScript. Languages: C++, Python, Julia, Javascript Homepage: https://opencv.org Q&A forum: https://forum.opencv.org/ Documentation: https://docs.opencv.org Source code: https://github.com/opencv Please pay special attention to our tutorials! https://docs.opencv.org/master Books about the OpenCV are described here: https://opencv.org/books.html -
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Automated Interpretability
Code for Language models can explain neurons in language models paper
...It includes a “neuron explainer” component that, given a target neuron or latent feature, proposes natural language explanations or heuristics (e.g. “this neuron activates when the input has property X”) and then simulates activation behavior across example inputs to test whether the explanation holds. The project also contains a “neuron viewer” web component for browsing neurons, explanations, and activation patterns, making it more interactive and exploratory. -
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KoboldCpp
Run GGUF models easily with a UI or API. One File. Zero Install.
KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, inspired by the original KoboldAI. It's a single self-contained distributable that builds off llama.cpp and adds many additional powerful features. -
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Fractal Neuro Oscillator
A fractal neural network
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QUCAS-PDP Astronomical Image Framework
NOTICE OF CONSOLIDATION & PARTNERSHIP PENDING As of April 2026, the 20
...Academic users at partner institutions (MTSU) are currently performing validation; all other commercial inquiries must contact the author QCI AstroEntangle Refiner – FDM soliton physics & image processing Magnetar QED Explorer – Magnetar fields, dark photons & vacuum QED Primordial Photon–DarkPhoton Entanglement – Von Neumann evolution in an expanding universe QCIS (Quantum Cosmology Integration Suite) – Quantum‑corrected cosmological perturbations 🔭 Overview These four projects form a complete computational framework for quantum‑inspired astrophysics. Together they enable: Image analysis of galaxy clusters (Abell, Bullet, etc.) using Fuzzy Dark Matter (FDM) soliton overlays. -
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Conscious Artificial Intelligence
It's possible for machines to become self-aware.
This project is a quest for conscious artificial intelligence. A number of prototypes will be developed as the project progresses. This project has 2 subprojects: Object Pascal based CAI NEURAL API - https://github.com/joaopauloschuler/neural-api Python based K-CAI NEURAL API - https://github.com/joaopauloschuler/k-neural-api A video from the first prototype has been made: http://www.youtube.com/watch?v=qH-IQgYy9zg Above video shows a popperian agent collecting mining ore from 3... -
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VALL-E X
Open source implementation of Microsoft's VALL-E X zero-shot TTS model
VALL-E-X is an open-source implementation of Microsoft’s VALL-E X zero-shot text-to-speech model, focused on multilingual, cross-lingual voice cloning. It is capable of synthesizing speech in English, Chinese, and Japanese from text while mimicking the voice characteristics of a speaker given only a short 3–10 second prompt. The model attempts to match not just timbre, but also tone, pitch, emotion, and prosody of the reference audio, resulting in highly personalized output. ... -
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Style Aligned
Official code for Style Aligned Image Generation via Shared Attention
...Instead of fully re-generating an image—and risking changes to lighting, texture, or rendering choices—the method aligns internal features across denoising steps so the target edit inherits the source style. This alignment acts like a constraint on the model’s evolution, steering composition, palette, and brushwork even as objects or attributes change. The result is more consistent edits across a set, which is crucial for workflows like product variations, character sheets, or brand-coherent art. The repository provides reproducible scripts, reference prompts, and guidance for tuning strengths so users can dial in subtle retouches or bolder substitutions. ... -
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TensorFlow Addons
Useful extra functionality for TensorFlow 2.x maintained by SIG-addons
...If you would like to maintain something, please feel free to submit a PR. We encourage multiple owners for all submodules. TensorFlow Addons is actively working towards forward compatibility with TensorFlow 2.x. -
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DALL-E 2 - Pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis
...Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as the denoising network) To train DALLE-2 is a 3 step process, with the training of CLIP being the most important. To train CLIP, you can either use x-clip package, or join the LAION discord, where a lot of replication efforts are already underway. Then, you will need to train the decoder, which learns to generate images based on the image embedding coming from the trained CLIP. -
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Transformer Reinforcement Learning X
A repo for distributed training of language models with Reinforcement
trlX is a distributed training framework designed from the ground up to focus on fine-tuning large language models with reinforcement learning using either a provided reward function or a reward-labeled dataset. Training support for Hugging Face models is provided by Accelerate-backed trainers, allowing users to fine-tune causal and T5-based language models of up to 20B parameters, such as facebook/opt-6.7b, EleutherAI/gpt-neox-20b, and google/flan-t5-xxl. For models beyond 20B parameters,... -
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LM Human Preferences
Code for the paper Fine-Tuning Language Models from Human Preferences
...The code is provided “as is” and explicitly says it may no longer run out-of-the-box due to dependencies or dataset migrations. It was tested on the smallest GPT-2 (124M parameters) under a specific environment (TensorFlow 1.x, specific CUDA / cuDNN combinations). It includes utilities for launching experiments, sampling from policies, and simple experiment orchestration. -
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Sockeye
Sequence-to-sequence framework, focused on Neural Machine Translation
...Developers may be interested in our developer guidelines. Starting with version 3.0.0, Sockeye is also based on PyTorch. We maintain backwards compatibility with MXNet models of version 2.3.x with 3.0.x. If MXNet 2.x is installed, Sockeye can run both with PyTorch or MXNet. All models trained with 2.3.x (using MXNet) can be converted to models running with PyTorch using the converter CLI (sockeye.mx_to_pt). -
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ConvNeXt V2
Code release for ConvNeXt V2 model
ConvNeXt V2 is an evolution of the ConvNeXt architecture that co-designs convolutional networks alongside self-supervised learning. The V2 version introduces a fully convolutional masked autoencoder (FCMAE) framework where parts of the image are masked and the network reconstructs the missing content, marrying convolutional inductive bias with powerful pretraining. -
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FrankMocap
A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
FrankMocap is a monocular 3D human capture system that estimates body, hand, and optionally face pose from a single RGB image or video. It regresses parametric human models (e.g., SMPL/SMPL-X) directly, producing temporally stable meshes and joint angles suitable for animation or analytics. The pipeline couples a robust 2D keypoint detector with 3D mesh regression networks and priors that keep results anatomically plausible. It can run frame-by-frame or with temporal smoothing, and includes demo apps for live webcam capture as well as batch processing. ...
