Search Results for "image analysis algorithm" - Page 2
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Showing 774 open source projects for "image analysis algorithm"
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1
Blurhash
Library to show colorful blurry placeholders while your content loads
... to be added as a field in a JSON object and to be stored in a database. The implementations are small and easy to port to new languages, and the end result is a smooth and interesting experience for your users. In short, BlurHash takes an image and gives you a short string (only 20-30 characters!) that represents the placeholder for this image. You do this on the backend of your service and store the string along with the image. -
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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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supervision
We write your reusable computer vision tools
We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us. -
4
iCn3D
Web-based protein structure viewer and analysis tool
"I see in 3D" (iCn3D) Structure Viewer is not only a web-based 3D viewer, but also a structure analysis tool interactively or in the batch mode using NodeJS scripts based on the npm package icn3d. iCn3D synchronizes the display of 3D structure, 2D interaction, and 1D sequences and annotations. Users' custom displays can be saved in a short URL or a PNG image. The complete package of iCn3D including Three.js and jQuery is in the directory "dist" after you get the source code with the "Code... -
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5
PHP Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic... -
6
google-java-format
Reformats Java source code to comply with Google Java Style
google-java-format is a program that reformats Java source code to comply with Google Java Style. The formatter can act on whole files, on limited lines, on specific offsets, passing through to standard-out (default) or altered in-place. There is no configurability as to the formatter's algorithm for formatting. This is a deliberate design decision to unify our code formatting on a single format. The formatter can be used in software that generates java to output more legible java code. Just... -
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Clair
Vulnerability Static Analysis for Containers
Clair is an application for parsing image contents and reporting vulnerabilities affecting the contents. This is done via static analysis and not at runtime. Clair v4 utilizes the ClairCore library as its engine for examining contents and reporting vulnerabilities. At a high level you can consider Clair a service wrapper to the functionality provided in the ClairCore library. The main branch may be in an unstable or even broken state during development. Please use releases instead of the main... -
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ML.NET
Open source and cross-platform machine learning framework for .NET
... that automates the process of building best performing models for your Machine Learning scenario. All you have to do is load your data, and AutoML takes care of the rest of the model building process. ML.NET has been designed as an extensible platform so that you can consume other popular ML frameworks (TensorFlow, ONNX, Infer.NET, and more) and have access to even more machine learning scenarios, like image classification, object detection, and more. -
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PipeCD
The One CD for All {applications, platforms, operations}
A GitOps style continuous delivery platform that provides consistent deployment and operations experience for any applications. Automatically trigger a new deployment when a defined event has occurred (e.g. container image pushed, helm chart published, etc). Support multiple application kinds on multi-cloud including Kubernetes, Terraform, Cloud Run, AWS Lambda, Amazon ECS. Support multiple analysis providers including Prometheus, Datadog, Stackdriver, and more. Easy to operate multi-cluster... -
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10
SAHI
A lightweight vision library for performing large object detection
... challenge in surveillance applications. Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult to detect using conventional detectors. In this work, an open-source framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection. -
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HugeGraph
A graph database that supports more than 100+ billion data
HugeGraph is a convenient, efficient, and adaptable graph database compatible with the Apache TinkerPop3 framework and the Gremlin query language. HugeGraph supports fast import performance in the case of more than 10 billion Vertices and Edges Graph, millisecond-level OLTP query capability, and can be integrated into big data platforms like Hadoop or Spark for OLAP analysis. The main scenarios of HugeGraph include correlation search, fraud detection, and knowledge graph. Not only supports... -
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Go Recipes
Collection of handy tools for Go projects
Visualize the distribution of code coverage in your project. This helps to identify code areas with high and low coverage. Useful when you have a large project with lots of files and packages. This 2D image-hash of your project should be more representative than a single number. For each module, the node representing the greatest version (i.e., the version chosen by Go's minimal version selection algorithm) is colored green. Other nodes, which aren't in the final build list, are colored grey... -
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Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase... -
14
Caire
Content aware image resize library
Caire is a content aware image resize library based on Seam Carving for Content-Aware Image Resizing paper. An energy map (edge detection) is generated from the provided image. The algorithm tries to find the least important parts of the image taking into account the lowest energy values. Using a dynamic programming approach the algorithm will generate individual seams across the image from top to down, or from left to right (depending on the horizontal or vertical resizing) and will allocate... -
15
dive
A tool for exploring each layer in a docker image
.... The lower left pane shows basic layer info and an experimental metric that will guess how much wasted space your image contains. This might be from duplicating files across layers, moving files across layers, or not fully removing files. Both a percentage "score" and total wasted file space is provided. You can build a Docker image and do an immediate analysis with one command: dive build -t some-tag . -
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fastdup
An unsupervised and free tool for image and video dataset analysis
fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale. -
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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 network... -
18
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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natural
General natural language facilities for node
"Natural" is a general natural language facility for nodejs. It offers a broad range of functionalities for natural language processing. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported. It’s still in the early stages, so we’re very interested in bug reports, contributions and the like. Note that many algorithms from Rob Ellis’s node-nltools are being merged into this project and will be maintained from here... -
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HDBSCAN
A high performance implementation of HDBSCAN clustering
..., is intuitive and easy to select. HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any). -
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GIXY
Nginx configuration static analyzer
... arguments with the help command: gixy --help. Gixy is available as a Docker image from the Docker hub. To use it, mount the configuration that you want to analyse as a volume and provide the path to the configuration file when running the Gixy image. If you have an image that already contains your nginx configuration, you can share the configuration with the Gixy container as a volume. -
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AtomAI
Deep and Machine Learning for Microscopy
... deployment of machine learning algorithms including deep convolutional neural networks, invariant variational autoencoders, and decomposition/unmixing techniques for image and hyperspectral data analysis. Ultimately, it aims to combine the power and flexibility of the PyTorch deep learning framework and the simplicity and intuitive nature of packages such as scikit-learn, with a focus on scientific data. -
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Code Climate CLI
Code Climate CLI
... with improved transparency across every level of your organization, and ensure optimal resource allocation for your engineering teams. Deliver high-quality code quickly and consistently to achieve true Continuous Delivery and out-innovate your competition. Improve your team’s processes and engineering skills to create a high-performance culture, boost pipeline efficiency, and increase employee engagement. code climate is a command-line interface for the Code Climate analysis platform. -
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Vearch
A distributed system for embedding-based vector retrieval
... 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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Learning Interpretability Tool
Interactively analyze ML models to understand their behavior
The Learning Interpretability Tool (LIT, formerly known as the Language Interpretability Tool) is a visual, interactive ML model-understanding tool that supports text, image, and tabular data. It can be run as a standalone server, or inside of notebook environments such as Colab, Jupyter, and Google Cloud Vertex AI notebooks.
