yolov5 dataset format conversion

[WangKeyi-Wky]

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Question

how to convert the formation of the dataset to the formation of yolov5?

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[UltralyticsAssistant]

👋 Hello @WangKeyi-Wky, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution:

• Tutorials: https://docs.ultralytics.com/yolov5/
• Custom Data Training: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/
• Hyperparameter Evolution: https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/
  This is an automated response 🤖 and an Ultralytics engineer will follow up to assist you soon.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results: https://docs.ultralytics.com/guides/model-training-tips/

For dataset format/conversion questions specifically, please include:
-A small sample (2–3 images) with their current annotations
-The target data.yaml and label examples you want to achieve
-The exact commands or scripts you’ve tried
-Your environment details (OS, Python, PyTorch, CUDA) and YOLOv5 version
-Any error messages or screenshots

Requirements

Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

• Python: https://www.python.org/
• requirements.txt: https://github.com/ultralytics/yolov5/blob/master/requirements.txt
• PyTorch: https://pytorch.org/get-started/locally/

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide: https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/
• Amazon Deep Learning AMI. See AWS Quickstart Guide: https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/
• Docker Image. See Docker Quickstart Guide: https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

• Actions: https://github.com/ultralytics/yolov5/actions
• train.py: https://github.com/ultralytics/yolov5/blob/master/train.py
• val.py: https://github.com/ultralytics/yolov5/blob/master/val.py
• detect.py: https://github.com/ultralytics/yolov5/blob/master/detect.py
• export.py: https://github.com/ultralytics/yolov5/blob/master/export.py
• benchmarks.py: https://github.com/ultralytics/yolov5/blob/master/benchmarks.py

Thanks for sharing more details so we can help you faster 🙌

[pderrenger]

Hi @WangKeyi-Wky! YOLOv5 expects datasets in a specific format with images in folders and corresponding .txt label files containing normalized bounding box coordinates in the format class x_center y_center width height.

You can check the YOLOv5 Train Custom Data tutorial which explains the required dataset structure and includes examples for converting from common formats like COCO, VOC, and others. The tutorial also shows how to set up your dataset.yaml configuration file.

If you're converting from a specific format like COCO or VOC, there are conversion scripts available in the YOLOv5 repository under the utils folder that can help automate the process.