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RizwanMunawar

yolov7-object-tracking

YOLOv7 Object Tracking Using PyTorch, OpenCV and Sort Tracking

ApplicationsObject detectionPython
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创建于 2022-08-21 · 更新于 2026-10-10 · 今日第 15448 名
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RizwanMunawarAdrielAmoguissonhun99

RizwanMunawar/yolov7-object-tracking 排名第 15,448，有 653 个星标。

同类别比较

RizwanMunawar/yolov7-object-tracking 排名第 15,448，此处有 8 个同类条目；bravekingzhang/flutter_chat_box 排名第 15,451。

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关键参数与规格
关键参数与规格
参数值
语言Python
类别Applications
子类别Object detection
关键词Computer vision
星标数653
分叉数175
贡献者11
创建日期2022-08-21
更新日期2026-10-10
1 天星标数0
7 天星标数-1
排名15,448
上次排名15,258
排名变化-190
7 天星标排名17,773
上次 7 天星标排名17,731
7 天星标排名变化-42
总星标排名15,691
上次总星标排名15,659
总星标排名变化-32
README 摘录

YOLOv7 Object Tracking 🚀 💥 **Ultralytics YOLOv8** support added 🚀 **YOLOv9, YOLOv10, YOLO11, YOLO12, YOLO13** support coming soon :) How to Run the Code 1. Create a virtual environment (Recommended to avoid conflicts): 4. **Output files** will be saved in with the original filename. | | | | | Download model weights automatically. | | | | | Source for inference (file, folder, or for webcam). | | | | | Inference image size in pixels. | | | | | Intersection over Union (IoU) threshold for NMS. | | | | | Display results during inference. | | | | | Save confidence scores in labels. | | | | | Use class-agnostic Non-Maximum Suppression (NMS

). | | | | | Directory to save results ( ). | | | | | Name of the results folder inside the project directory. | | | | | Allow existing project/name without incrementing. | | | | | Do not trace the model during export. | | | | | Assign a unique color to each track for visualization. | | | | | Save bounding box dimensions in tracks. | | | | | Save results with object ID in files. | Results 📊 Star History References YOLOv7 GitHub SORT GitHub **Some of my articles/research papers | computer vision awesome resources for learning | How do I appear to the world?

常见问题

记录了多少星标和分叉？

RizwanMunawar/yolov7-object-tracking 有 653 个星标和 175 个分叉。

仓库排名是多少？

RizwanMunawar/yolov7-object-tracking 排名第 15,448。

记录的语言和类别是什么？

RizwanMunawar/yolov7-object-tracking 有 653 个星标，语言为 Python，类别为 Applications。

哪个仓库排名最接近？

RizwanMunawar/yolov7-object-tracking 与 bravekingzhang/flutter_chat_box 比较，后者排名第 15,451，有 653 个星标。

README

YOLOv7 Object Tracking 🚀

💥 Ultralytics YOLOv8 support added python detect.py --weights yolov8n.pt

🚀 YOLOv9, YOLOv10, YOLO11, YOLO12, YOLO13 support coming soon :)

How to Run the Code

  1. Clone the repository:

    git clone https://github.com/RizwanMunawar/yolov7-object-tracking.git

  2. Navigate to the cloned folder:

    cd yolov7-object-tracking

  3. Create a virtual environment (Recommended to avoid conflicts):

    For Anaconda:

    conda create -n yolov7objtracking python=3.12
conda activate yolov7objtracking

    For Linux:

    python3 -m venv yolov7objtracking
source yolov7objtracking/bin/activate

    For Windows:

    python3 -m venv yolov7objtracking
cd yolov7objtracking/Scripts
activate

    For MacOS:

    python3 -m venv yolov7objtracking
source yolov7objtracking/bin/activate

  4. Update pip and install dependencies:

    pip install --upgrade pip
pip install -r requirements.txt

  5. Run the script:

    Select the appropriate command based on your requirements. Pretrained yolov7 weights will be downloaded automatically if needed.

    • Detection only:

      python detect.py --weights yolov7.pt

# If you want to use your own videos.
python detect.py --weights yolov7.pt --source "your video.mp4"

# For Inference with YOLOv8
python detect.py --weights yolov8n.pt

    • Object tracking:

      python detect_and_track.py --weights yolov7.pt

# If you want to use your own videos.
python detect_and_track.py --weights yolov7.pt --source "your video.mp4"

    • Webcam:

      python detect_and_track.py --weights yolov7.pt --source 0

    • External Camera:

      python detect_and_track.py --weights yolov7.pt --source 1

    • IP Camera Stream:

      python detect_and_track.py --source "your IP Camera Stream URL" --device 0

    • Specific class tracking (e.g., person):

      python detect_and_track.py --weights yolov7.pt --source "your video.mp4" --classes 0

    • Colored tracks:

      python detect_and_track.py --weights yolov7.pt --source "your video.mp4" --colored-trk

    • Save track centroids, IDs, and bounding box coordinates:

      python detect_and_track.py --weights yolov7.pt --source "your video.mp4" --save-txt --save-bbox-dim

  6. Output files will be saved in runs/detect/obj-tracking with the original filename.

Arguments details 🚀

Argument Type Default Description
--weights str yolov7.pt Path(s) to model weights (.pt file).
--download flag False Download model weights automatically.
--source str None Source for inference (file, folder, or 0 for webcam).
--img-size int 640 Inference image size in pixels.
--conf-thres float 0.25 Object confidence threshold.
--iou-thres float 0.45 Intersection over Union (IoU) threshold for NMS.
--device str '' CUDA device (e.g., 0 or 0,1,2,3) or cpu.
--view-img flag False Display results during inference.
--save-txt flag False Save results to .txt files.
--save-conf flag False Save confidence scores in .txt labels.
--nosave flag False Do not save images or videos.
--classes list[int] None Filter results by class (e.g., --classes 0 or --classes 0 2 3).
--agnostic-nms flag False Use class-agnostic Non-Maximum Suppression (NMS).
--augment flag False Enable augmented inference.
--update flag False Update all models.
--project str runs/detect Directory to save results (project/name).
--name str runs/detect/object_tracking Name of the results folder inside the project directory.
--exist-ok flag False Allow existing project/name without incrementing.
--no-trace flag False Do not trace the model during export.
--colored-trk flag False Assign a unique color to each track for visualization.
--save-bbox-dim flag False Save bounding box dimensions in .txt tracks.
--save-with-object-id flag False Save results with object ID in .txt files.

Results 📊

YOLOv7 Detection Only

YOLOv7 Object Tracking with ID

YOLOv7 Object Tracking with ID and Label

Star History

References

Some of my articles/research papers | computer vision awesome resources for learning | How do I appear to the world? 🚀

Ultralytics YOLO11: Object Detection and Instance Segmentation🤯

Parking Management using Ultralytics YOLO11

My 🖐️Computer Vision Hobby Projects that Yielded Earnings

Best Resources to Learn Computer Vision

Roadmap for Computer Vision Engineer

How did I spend 2022 in the Computer Vision Field

Domain Feature Mapping with YOLOv7 for Automated Edge-Based Pallet Racking Inspections

Exudate Regeneration for Automated Exudate Detection in Retinal Fundus Images

Feature Mapping for Rice Leaf Defect Detection Based on a Custom Convolutional Architecture

Yolov5, Yolo-x, Yolo-r, Yolov7 Performance Comparison: A Survey

Explainable AI in Drug Sensitivity Prediction on Cancer Cell Lines

Train YOLOv8 on Custom Data

Session with Ultralytics Team about Computer Vision Journey

Contributors 🤝

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