YOLOv7 object detection in Ruby in 10 minutes

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • netron

    Visualizer for neural network, deep learning and machine learning models

    Browser: Start the browser version.

  • onnxruntime-ruby

    Run ONNX models in Ruby

    View on GitHub

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  • onnxruntime

    ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

    🔥 ONNX Runtime - the high performance scoring engine for ML models - for Ruby

  • models

    A collection of pre-trained, state-of-the-art models in the ONNX format (by onnx)

    Download pre-trained models from the ONNX Model Zoo

  • ONNX-YOLOv7-Object-Detection

    Python scripts performing object detection using the YOLOv7 model in ONNX.

    git clone https://github.com/ibaiGorordo/ONNX-YOLOv7-Object-Detection.git cd ONNX-YOLOv7-Object-Detection pip install -r requirements.txt

  • PINTO_model_zoo

    A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.

    Download the ONNX model from this project: 307_YOLOv7

  • RMagick

    Ruby bindings for ImageMagick

    mini_magick is much slower than YOLO. I hear that rmagick is well maintained these days, so you may want to use that.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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