Licensing is neither feasible nor effective for addressing AI risks provides machine-learning models, research, training resources, or evaluation…
open-solution-salt-identification
A repository archived by its owner and now read-only. It is an open solution to the TGS Salt Identification Challenge. The project is listed with data science and image segmentation topics. The readme states that support can no longer be provided.
GitHub repo Directory import
Features
- open solution to the TGS Salt Identification Challenge Source: github.com
Limitations
- repository was archived by the owner on Mar 22, 2021 Source: github.com
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0Licensing is neither feasible nor effective for addressing AI risks provides machine-learning models, research, training resources, or evaluation…
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0A portfolio page for Greg (Grzegorz) Surma that lists side projects related to machine learning, computer vision, self-driving cars and iOS development. It indexes apps and experiments, including AI avatars, image filters, depth estimators, a CNN explainer and games, with the tools and platforms used for each.
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0Apache OpenNLP is a machine learning based toolkit for the processing of natural language text. It supports tasks such as sentence segmentation, tokenization, lemmatization, part-of-speech tagging, named entity extraction, chunking, parsing, language detection and coreference resolution. The project is developed by volunteers and welcomes contributions, from small documentation fixes to new components.
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0Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. It provides high-level APIs in Java, Scala, Python and R, and supports Spark SQL, pandas API on Spark, MLlib, GraphX and Structured Streaming. The documentation covers downloads, the shell and client applications.
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0OpenFace is a free and open source face recognition implementation with deep neural networks, written in Python and Torch and based on the CVPR 2015 FaceNet paper. It detects faces, aligns them, and embeds faces on a 128-dimensional unit hypersphere.
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0MMDeploy is documented at version 1.3.1 and covers getting started, converting, running, evaluating and quantizing models, building from source or Docker image, and SDK usage. Its backend support pages describe ncnn, ONNX Runtime, OpenVINO, PPLNN, SNPE, TensorRT, TorchScript, RKNN, TVM and Core ML, and its codebase support covers several OpenMMLab projects.
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