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👋 Hello @lqh964165950, thank you for your interest in YOLOv5 🚀!
If you're looking to add FPS and mAP evaluation metrics in YOLOv5, please make sure you are familiar with the evaluation process outlined in our official documentation.
If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it. This helps us understand the context better and provide a more accurate solution to the issue.
For custom training ❓ questions, please provide as much detail as possible, including examples of your dataset images and any training logs you have. Ensure you are following our guidelines for best training results, which cover topics like model hyperparameters and data preparation.
Requirements
To run YOLOv5, you need Python>=3.8.0 with all required dependencies installed. You can set up your environment by cloning the YOLOv5 repository and installing the dependencies listed in the requirements file.
Environments
YOLOv5 can be executed in various environments such as cloud-based notebooks with free GPU support, Google Cloud, Amazon Deep Learning AMI, and through Docker images.
Status
If you check the status badge in our GitHub repository, it indicates whether all Continuous Integration (CI) tests are currently passing. This badge gives you a quick overview of the repository's health and functionality.
This is an automated response, but rest assured an Ultralytics engineer will assist you soon! 😊
@lqh964165950 to add FPS and mAP evaluation metrics in YOLOv5, you can utilize the validation script val.py for mAP calculations. FPS can be measured by running inference using detect.py while timing the inference process. For customized benchmarking, consider integrating tools and metrics into val.py or using external profiling libraries. You may find these guides helpful:
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Question
如何在yolov5中添加FPS和mAPs评价指标?
Additional
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