ultralytics / yolov5

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
https://docs.ultralytics.com
GNU Affero General Public License v3.0
50.45k stars 16.28k forks source link

Continue training #13378

Open pjh11214 opened 2 days ago

pjh11214 commented 2 days ago

Search before asking

Question

How to continue training on the basis of already trained models

Additional

No response

UltralyticsAssistant commented 2 days ago

👋 Hello @pjh11214, thank you for reaching out and showing interest in YOLOv5 🚀! Continuing training from a pretrained model is a common task and I'm here to help guide you. An Ultralytics engineer will also assist you soon.

To continue training with an already trained model, you generally need to specify the path to your weight file using the --weights argument when executing the training script. This typically looks something like this:

python train.py --weights path/to/your_weights.pt --cfg path/to/your_custom_config.yaml --data path/to/your_data.yaml --epochs 50

To ensure you've set everything up properly, here's a brief reminder about the environment requirements:

Requirements

Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. You can set up your environment as follows:

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

If this is a 🐛 bug report, please provide a minimum reproducible example to help us identify the issue. For questions involving custom training, supplying additional information like dataset examples and training logs can be immensely helpful.

Feel free to check out these helpful resources for more detailed guidance:

Explore our new YOLOv8 for advanced features and capabilities:

Introducing YOLOv8 🚀

We're thrilled to present our state-of-the-art model, YOLOv8! Designed for enhanced performance in object detection, segmentation, and classification tasks.

Get started with YOLOv8 by running:

pip install ultralytics

Remember, our community and resources are here to help you succeed with your machine learning endeavors! 🚀