Search Results for "image genetic algorithm"
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Showing 14 open source projects for "image genetic algorithm"
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PyGAD
Source code of PyGAD, Python 3 library for building genetic algorithms
PyGAD is an open-source easy-to-use Python 3 library for building the genetic algorithm and optimizing machine learning algorithms. It supports Keras and PyTorch. PyGAD supports optimizing both single-objective and multi-objective problems. PyGAD supports different types of crossover, mutation, and parent selection. PyGAD allows different types of problems to be optimized using the genetic algorithm by customizing the fitness function. -
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Imagen - Pytorch
Implementation of Imagen, Google's Text-to-Image Neural Network
Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior... -
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Smile
Statistical machine intelligence and learning engine
Smile is a fast and comprehensive machine learning engine. With advanced data structures and algorithms, Smile delivers the state-of-art performance. Compared to this third-party benchmark, Smile outperforms R, Python, Spark, H2O, xgboost significantly. Smile is a couple of times faster than the closest competitor. The memory usage is also very efficient. If we can train advanced machine learning models on a PC, why buy a cluster? Write applications quickly in Java, Scala, or any JVM... -
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MegEngine
Easy-to-use deep learning framework with 3 key features
MegEngine is a fast, scalable and easy-to-use deep learning framework with 3 key features. You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR algorithm. ... -
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Vearch
A distributed system for embedding-based vector retrieval
...End-to-end one-click deployment. Through the module of the plugin, a complete default visual search system can be deployed just with one click. Otherwise, you can easily customize your own image, video, or text feature extraction algorithm plugin. This GIF provides a clear demonstration of the project vearch usage and its internal structure. The use of vearch is mainly divided into three steps. Firstly, create DB and Space, then import your data, and finally, you can search on your own dataset. -
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BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events... -
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CNN Explainer
Learning Convolutional Neural Networks with Interactive Visualization
In machine learning, a classifier assigns a class label to a data point. For example, an image classifier produces a class label (e.g, bird, plane) for what objects exist within an image. A convolutional neural network, or CNN for short, is a type of classifier, which excels at solving this problem! A CNN is a neural network: an algorithm used to recognize patterns in data. Neural Networks in general are composed of a collection of neurons that are organized in layers, each with their own learnable weights and biases. ... -
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Computer Vision Pretrained Models
A collection of computer vision pre-trained models
...Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application. For example, if you want to build a self-learning car. You can spend years building a decent image recognition algorithm from scratch or you can take the inception model (a pre-trained model) from Google which was built on ImageNet data to identify images in those pictures. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone. ... -
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easy12306
Automatic recognition of 12306 verification code
Automatic recognition of 12306 verification code using machine learning algorithm. Identify never-before-seen pictures. -
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Swift AI
The Swift machine learning library
Swift AI is a high-performance deep learning library written entirely in Swift. We currently offer support for all Apple platforms, with Linux support coming soon. Swift AI includes a collection of common tools used for artificial intelligence and scientific applications. A flexible, fully-connected neural network with support for deep learning. Optimized specifically for Apple hardware, using advanced parallel processing techniques. We've created some example projects to demonstrate the... -
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GA-EoC
GeneticAlgorithm-based search for Heterogeneous Ensemble Combinations
...To enhance classification performances, we propose an ensemble of classifiers that combine the classification outputs of base classifiers using the simplest and largely used majority voting approach. Instead of creating the ensemble using all base classifiers, we have implemented a genetic algorithm (GA) to search for the best combination from heterogeneous base classifiers. The classification performances achieved by the proposed method method on the chosen datasets are promising. -
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Reactor Breeder
A Genetic Algorithm for Reactors in StarMade
This software uses a genetic algorithm to "evolve" reactor designs for Schema's space-simulation game, Star-Made (http://star-made.org/). One of the more unique aspects of the game is that ship power management is not as simple as filling a cube with power generation blocks. This mechanism leads to difficulties in getting maximal power generation out of such reactors. -
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TrimGA is a lightweight genetic algorithm library written in pure Java 6.0 that can be quickly applied to most optimization problems.
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