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chuanenlin/drone-net

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View on GitHub↗
0 stars·0 forks·8 views

Drone Net

DroneNet is Joseph Redmon's YOLO real-time object detection system retrained on 2664 images of DJI drones, labeled. The original and labeled images used for retraining can be found under the image and label folders respectively.

Features

  • Computer Vision - Detects quadcopters in images using deep learning.

Star history

Star history chart for chuanenlin/drone-netStar history chart for chuanenlin/drone-net

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Drone Net

These projects share indexed features with Drone Net. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • achaiah/pywickachaiah avatar

    achaiah/pywick

    400View on GitHub↗

    High-level batteries-included neural network training library for Pytorch

    Python
    View on GitHub↗400
  • ailab-cvc/yolo-worldAILab-CVC avatar

    AILab-CVC/YOLO-World

    6,425View on GitHub↗

    YOLO-World is a vision-language framework and open-vocabulary object detection model. It identifies objects in images and video based on free-form text prompts without requiring predefined category labels. The system enables the identification of arbitrary objects by fusing image features with text embeddings. It includes a specialized tool for automated image labeling, which generates bounding box annotations for custom datasets using text-based prompts. The project provides a deployment pipeline for converting models into quantized ONNX and TFLite formats, supporting real-time inference on

    Python
    View on GitHub↗6,425
  • airctic/icevisionA

    airctic/icevision

    0View on GitHub↗
    View on GitHub↗0
  • 1adrianb/face-alignment1adrianb avatar

    1adrianb/face-alignment

    7,518View on GitHub↗

    This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a facial landmark detector and reconstruction tool, utilizing deep learning to identify precise geometric points on human faces from image datasets. The library allows for the selection of specific detection backends to balance accuracy and processing speed. It supports the integration of precomputed bounding box files, which enables the system to bypass the initial detection phase and proceed directly to landmark extraction. The toolkit includes capabilities for batch image p

    Python
    View on GitHub↗7,518
Compare all 30 related projects→

Frequently asked questions

What does chuanenlin/drone-net do?

DroneNet is Joseph Redmon's YOLO real-time object detection system retrained on 2664 images of DJI drones, labeled. The original and labeled images used for retraining can be found under the image and label folders respectively.

What are the main features of chuanenlin/drone-net?

The main features of chuanenlin/drone-net are: Computer Vision.

Which projects share features with chuanenlin/drone-net?

Projects with overlapping indexed features include: achaiah/pywick — High-level batteries-included neural network training library for Pytorch. ailab-cvc/yolo-world — YOLO-World is a vision-language framework and open-vocabulary object detection model. It identifies objects in images… airctic/icevision. alankbi/detecto — Build fully-functioning computer vision models with PyTorch. albu/albumentations — Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for… 1adrianb/face-alignment — This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a…