Search Results for "yolov4.weights"
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Showing 36 open source projects for "yolov4.weights"
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Job Recommend
The basics of building a job recommendation workflow
...You can study how to transform raw text into features and how to evaluate simple heuristics or baseline models. The code encourages experimentation, inviting you to swap scoring rules, adjust weights, or plug in alternative representations. It serves as a starting point for understanding recommendation pipelines before moving to production-grade systems. -
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Grey Wolf Optimizer for Path Planning
Grey Wolf Optimizer (GWO) path planning/trajectory
...The tool provides built-in functions to configure different UAV environments and supports multiple optimization objectives. It includes progress visualization to help monitor the optimization process during simulations. Users can adjust objective function weights and experiment with multiple heuristic search strategies to explore optimal solutions. This project demonstrates applications in multi-agent and multi-UAV cooperative path planning, making it useful for research and educational purposes in the field of intelligent optimization and robotics. -
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PowerGlitch
Tiny JS library to glitch anything on the web
PowerGlitch is a standalone library with no external dependencies. It leverages CSS animations to glitch anything on the web, without using a canvas. It weights less than 2kb minified and gzipped. -
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Open LLMs
A list of open LLMs available for commercial use
...For teams or developers interested in experimenting with LLMs but wanting to avoid vendor lock-in or licensing constraints, open-llms offers a practical starting point. It aggregates metadata, licensing info, and often pointers to the model weights or model cards — helping users quickly compare models by size, license, domain, and capabilities. By compiling this in one place, open-llms reduces friction in exploring the LLM space, making it easier to try different models, benchmark them, or build custom applications. -
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AIMET
AIMET is a library that provides advanced quantization and compression
Qualcomm Innovation Center (QuIC) is at the forefront of enabling low-power inference at the edge through its pioneering model-efficiency research. QuIC has a mission to help migrate the ecosystem toward fixed-point inference. With this goal, QuIC presents the AI Model Efficiency Toolkit (AIMET) - a library that provides advanced quantization and compression techniques for trained neural network models. AIMET enables neural networks to run more efficiently on fixed-point AI hardware... -
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NetworkX
Network analysis in Python
...Generators for classic graphs, random graphs, and synthetic networks. Nodes can be "anything" (e.g., text, images, XML records). Edges can hold arbitrary data (e.g., weights, time-series). Open source 3-clause BSD license. Well tested with over 90% code coverage. Additional benefits from Python include fast prototyping, easy to teach, and multi-platform. Find the shortest path between two nodes in an undirected graph. Python’s None object is not allowed to be used as a node. It determines whether optional function arguments have been assigned in many functions. ... -
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Neutralinojs
Portable and lightweight cross-platform desktop application
...In Electron and NWjs, you have to install Node.js and hundreds of dependency libraries. Embedded Chromium and Node.js make simple apps bloaty, in most scenarios, framework weights more than your app source. Neutralinojs offers a lightweight and portable SDK which is an alternative for Electron and NW.js. Neutralinojs doesn't bundle Chromium and uses the existing web browser library in the operating system (Eg: gtk-webkit2 on Linux). Neutralinojs implements a secure WebSocket connection for native operations and embeds a static web server to serve the web content. ... -
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ThetaGang
ThetaGang is an IBKR bot for collecting money
...This strategy reduces risk, but may also limit gains from big market swings. By reducing risk, one can increase leverage. ThetaGang will try to acquire your desired allocation of each stock or ETF according to the weights you specify in the config. To acquire the positions, the script will write puts when conditions are met. -
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Iosevka
Versatile typeface for code, from code
Iosevka is an open-source, sans-serif + slab-serif, monospace + quasi‑proportional typeface family, designed for writing code, using in terminals, and preparing technical documents. The Iosevka’s monospace family is provided in a slender outfit by default: glyphs are exactly 1/2em wide. Compared to the competitors, you could fit more columns within the same screen width. Iosevka provides two widths, Normal and Extended. If you prefer more breeze between the character, choose Extended and... -
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wger
Self hosted FLOSS fitness/workout, nutrition and weight tracker
...Create your personal diet plan by creating as many meals with as many different ingredients as you need. The application will calculate the nutritional values (total energy, proteins, carbohydrates, etc.) of the entire plan and of each of the meals. Enter the weights and reps you've done for each exercise to generate diagrams that let you see at a glance how well you're doing. Of course, the raw numbers are still accessible. -
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ngraph.path
Path finding in a graph
ngraph.path is a JavaScript library that implements efficient pathfinding algorithms for graphs, primarily designed to compute shortest paths in weighted or unweighted networks. It provides a clean API for constructing graph models, assigning weights to edges, and querying for optimal routes between nodes, making it useful for routing, games, maps, and network optimization. The library includes several algorithm implementations such as A*, Dijkstra’s, and breadth-first search, each suited to different types of graph structure and performance needs. It can be integrated with visualization libraries like VivaGraphJS to animate or highlight computed paths in a rendered graph, enabling interactive routing features. ... -
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NanoNeuron
NanoNeuron is 7 simple JavaScript functions
Nano-Neuron is a didactic project that reduces the idea of a neuron to a handful of tiny JavaScript functions so learners can see “learning” in action without heavy frameworks. It demonstrates how a scalar input can be linearly transformed with a weight and bias, then adjusted via gradient updates to fit a simple mapping such as Celsius-to-Fahrenheit conversion. The code emphasizes readability over performance, inviting you to step through calculations and watch parameters converge. Because... -
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Theseus
A library for differentiable nonlinear optimization
...Problems are expressed as factor graphs with variables on manifolds (e.g., SE(3), SO(3)), so classical robotics and vision tasks—bundle adjustment, pose graph optimization, hand–eye calibration—can be written succinctly and solved efficiently. Because solves are differentiable, you can backpropagate through optimization to learn cost weights, feature extractors, or initialization networks end-to-end. The implementation supports batched optimization on GPU, robust losses, damping strategies, and custom factors, making it practical for real-time systems. Helper packages provide geometry primitives and utilities for composing priors, relative constraints, and measurement models. ... -
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weight_matchers
Efficiently find items by matching weight in const lookup structure.
...If your data is static, you can build the lookup structure (a complete binary tree) at compile time, by making it `const`. You can use any inferred numeric type for the weights. You can have any range for the lookup, by default `0.0 .. 1.0` for floats, and their respective whole spectrum for unsigned integers. -
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Neural Network Visualization
Project for processing neural networks and rendering to gain insights
...It provides an interactive, graphical representation of how data flows through neural network layers, offering a unique educational experience for those new to deep learning or looking to explain it visually. By animating input, weights, activations, and outputs, the tool demystifies neural network operations and helps users intuitively grasp complex concepts. Its lightweight codebase is great for customization and teaching purposes. -
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Minkowski Engine
Auto-diff neural network library for high-dimensional sparse tensors
...Compressing a neural network to speed up inference and minimize memory footprint has been studied widely. One of the popular techniques for model compression is pruning the weights in convnets, is also known as sparse convolutional networks. Such parameter-space sparsity used for model compression compresses networks that operate on dense tensors and all intermediate activations of these networks are also dense tensors. -
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TransPose
PyTorch Implementation for "TransPose, Keypoint localization
TransPose is a human pose estimation model based on a CNN feature extractor, a Transformer Encoder, and a prediction head. Given an image, the attention layers built in Transformer can efficiently capture long-range spatial relationships between keypoints and explain what dependencies the predicted keypoints locations highly rely on. -
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PyTorch SimCLR
PyTorch implementation of SimCLR: A Simple Framework
...And most important, their features are known to adapt well to new problems. This is particularly interesting when annotated training data is scarce. In situations like this, we take the models’ pre-trained weights, append a new classifier layer on top of it, and retrain the network. This is called transfer learning, and is one of the most used techniques in CV. Aside from a few tricks when performing fine-tuning (if the case), it has been shown (many times) that if training for a new task, models initialized with pre-trained weights tend to learn faster and be more accurate then training from scratch using random initialization. -
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TFLearn
Deep learning library featuring a higher-level API for TensorFlow
...Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations, and more. Effortless device placement for using multiple CPU/GPU. The high-level API currently supports the most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, etc. -
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Duckling (Old)
Clojure library that parses text into structured data
Duckling (the “old” archived version) is a natural language processing library (in Clojure) for parsing text to structured data — specifically, recognizing quantities such as dates, times, durations, measurements, currencies, etc., from free-form text. To use Duckling in your project, you just need two functions: load! to load the default configuration, and parse to parse a string. Duckling is a Clojure library that parses text into structured data. See our blog post announcement for more... -
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NN-SVG
Publication-ready NN-architecture schematics
Illustrations of Neural Network architectures are often time-consuming to produce, and machine learning researchers all too often find themselves constructing these diagrams from scratch by hand. NN-SVG is a tool for creating Neural Network (NN) architecture drawings parametrically rather than manually. It also provides the ability to export those drawings to Scalable Vector Graphics (SVG) files, suitable for inclusion in academic papers or web pages. The tool provides the ability to... -
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Bender
Easily craft fast Neural Networks on iOS
...Bender provides the ease of use of CoreML with the flexibility of a modern ML framework. Bender allows you to run trained models, you can use Tensorflow, Keras, Caffe, the choice is yours. Either freeze the graph or export the weights to files. You can import a frozen graph directly from supported platforms or re-define the network structure and load the weights. Either way, it just takes a few minutes. Bender suports the most common ML nodes and layers but it is also extensible so you can write your own custom functions. With Core ML, you can integrate trained machine learning models into your app, it supports Caffe and Keras 1.2.2+ at the moment. ... -
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Image classification models for Keras
Keras code and weights files for popular deep learning models
...For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet' argument in model constructor for all image models, weights='msd' for the music tagging model). Weights are automatically downloaded if necessary, and cached locally in ~/.keras/models/. This repository contains code for the following Keras models, VGG16, VGG19, ResNet50, Inception v3, and CRNN for music tagging. -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu... -
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Priority Estimation Tool (AHP)
PriEsT is a decision making tool for Analytic Hierarchy Process (AHP).
...In PriEsT, you enter a list of available options and then define your criteria for prioritization. After defining criteria, PriEsT allows you to enter your judgements against each criterion, which are then used to calculate the final ranking (or weights). Please cite this if you find it useful:- Siraj, S., Mikhailov, L. and Keane, J. A. (2015), "PriEsT: an interactive decision support tool to estimate priorities from pairwise comparison judgments". International Transactions in Operational Research. 22: 217–235. doi:10.1111/itor.12054
