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light-detection-model

Using the python library YOLOV8 this model can detect traffic lights and the state of the traffic light

https://github.com/naligant/light-detection-model

Science Score: 54.0%

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    CITATION.cff file
    Found CITATION.cff file
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    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
    Links to: arxiv.org, zenodo.org
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    Scientific vocabulary similarity
    Low similarity (10.7%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Using the python library YOLOV8 this model can detect traffic lights and the state of the traffic light

Basic Info
  • Host: GitHub
  • Owner: naligant
  • License: gpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 142 KB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme Contributing License Citation

README.md

[English](README.md) | [简体中文](README.zh-CN.md)
Ultralytics CI YOLOv8 Citation Docker Pulls
Run on Gradient Open In Colab Open In Kaggle

[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics), developed by [Ultralytics](https://ultralytics.com), is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image classification tasks. To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).

Documentation

See below for quickstart intallation and usage example, and see the YOLOv8 Docs for full documentation on training, validation, prediction and deployment.

Install Pip install the ultralytics package including all [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.7.0**](https://www.python.org/) environment, including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). ```bash pip install ultralytics ```
Usage YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command: ```bash yolo task=detect mode=predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" ``` `yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/config/) in the YOLOv8 [Docs](https://docs.ultralytics.com). ```bash yolo task=detect mode=train model=yolov8n.pt args... classify predict yolov8n-cls.yaml args... segment val yolov8n-seg.yaml args... export yolov8n.pt format=onnx args... ``` YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/config/) as in the CLI example above: ```python from ultralytics import YOLO # Load a model model = YOLO("yolov8n.yaml") # build a new model from scratch model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training) # Use the model results = model.train(data="coco128.yaml", epochs=3) # train the model results = model.val() # evaluate model performance on the validation set results = model("https://ultralytics.com/images/bus.jpg") # predict on an image success = YOLO("yolov8n.pt").export(format="onnx") # export a model to ONNX format ``` [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/yolo/v8/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/ultralytics/releases). ### Known Issues / TODOs We are still working on several parts of YOLOv8! We aim to have these completed soon to bring the YOLOv8 feature set up to par with YOLOv5, including export and inference to all the same formats. We are also writing a YOLOv8 paper which we will submit to [arxiv.org](https://arxiv.org) once complete. - [ ] TensorFlow exports - [ ] DDP resume - [ ] [arxiv.org](https://arxiv.org) paper

Checkpoints

All YOLOv8 pretrained models are available here. Detection and Segmentation models are pretrained on the COCO dataset, while Classification models are pretrained on the ImageNet dataset.

Models download automatically from the latest Ultralytics release on first use.

Detection | Model | size
(pixels) | mAPval
50-95 | Speed
CPU
(ms) | Speed
T4 GPU
(ms) | params
(M) | FLOPs
(B) | | ----------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- | | [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n.pt) | 640 | 37.3 | - | - | 3.2 | 8.7 | | [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s.pt) | 640 | 44.9 | - | - | 11.2 | 28.6 | | [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m.pt) | 640 | 50.2 | - | - | 25.9 | 78.9 | | [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l.pt) | 640 | 52.9 | - | - | 43.7 | 165.2 | | [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x.pt) | 640 | 53.9 | - | - | 68.2 | 257.8 | - **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
Reproduce by `yolo mode=val task=detect data=coco.yaml device=0` - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
Reproduce by `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu`
Segmentation | Model | size
(pixels) | mAPbox
50-95 | mAPmask
50-95 | Speed
CPU
(ms) | Speed
T4 GPU
(ms) | params
(M) | FLOPs
(B) | | --------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- | | [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | - | - | 3.4 | 12.6 | | [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | - | - | 11.8 | 42.6 | | [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | - | - | 27.3 | 110.2 | | [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | - | - | 46.0 | 220.5 | | [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | - | - | 71.8 | 344.1 | - **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
Reproduce by `yolo mode=val task=detect data=coco.yaml device=0` - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
Reproduce by `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu`
Classification | Model | size
(pixels) | acc
top1 | acc
top5 | Speed
CPU
(ms) | Speed
T4 GPU
(ms) | params
(M) | FLOPs
(B) at 640 | | --------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------- | ---------------------------- | ------------------ | ------------------------ | | [YOLOv8n](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | - | - | 2.7 | 4.3 | | [YOLOv8s](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | - | - | 6.4 | 13.5 | | [YOLOv8m](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | - | - | 17.0 | 42.7 | | [YOLOv8l](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | - | - | 37.5 | 99.7 | | [YOLOv8x](https://github.com/ultralytics/ultralytics/releases/download/v8.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | - | - | 57.4 | 154.8 | - **mAPval** values are for single-model single-scale on [ImageNet](https://www.image-net.org/) dataset.
Reproduce by `yolo mode=val task=detect data=coco.yaml device=0` - **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
Reproduce by `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu`

Integrations




| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW | | :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | | Label and export your custom datasets directly to YOLOv8 for training with Roboflow | Automatically track, visualize and even remotely train YOLOv8 using ClearML (open-source!) | Free forever, Comet lets you save YOLOv8 models, resume training, and interactively visualise and debug predictions | Run YOLOv8 inference up to 6x faster with Neural Magic DeepSparse |

Ultralytics HUB

Ultralytics HUB is our ⭐ NEW no-code solution to visualize datasets, train YOLOv8 🚀 models, and deploy to the real world in a seamless experience. Get started for Free now! Also run YOLOv8 models on your iOS or Android device by downloading the Ultralytics App!

Contribute

We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our Contributing Guide to get started, and fill out our Survey to send us feedback on your experience. Thank you 🙏 to all our contributors!

License

YOLOv8 is available under two different licenses:

  • GPL-3.0 License: See LICENSE file for details.
  • Enterprise License: Provides greater flexibility for commercial product development without the open-source requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at Ultralytics Licensing.

Contact

For YOLOv8 bugs and feature requests please visit GitHub Issues. For professional support please Contact Us.


Owner

  • Login: naligant
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use this software, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  - family-names: Chaurasia
    given-names: Ayush
    orcid: "https://orcid.org/0000-0002-7603-6750"
  - family-names: Qiu
    given-names: Jing
    orcid: "https://orcid.org/0000-0003-3783-7069"
  title: "YOLO by Ultralytics"
  version: 8.0.0
  # doi: 10.5281/zenodo.3908559  # TODO
  date-released: 2023-1-10
  license: GPL-3.0
  url: "https://github.com/ultralytics/ultralytics"

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Dependencies

docker/Dockerfile docker
  • nvcr.io/nvidia/pytorch 22.12-py3 build
requirements.txt pypi
  • GitPython >=3.1.24
  • Pillow >=7.1.2
  • PyYAML >=5.3.1
  • hydra-core >=1.2.0
  • ipython *
  • matplotlib >=3.2.2
  • numpy >=1.18.5
  • opencv-python >=4.1.1
  • pandas >=1.1.4
  • psutil *
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • tensorboard >=2.4.1
  • thop >=0.1.1
  • torch >=1.7.0
  • torchvision >=0.8.1
  • tqdm >=4.64.0
setup.py pypi