Regarding precision,recalland map50 metric about yolov5 HOT 7 OPEN

Hello! 😊 Great to hear you're experimenting with combining YOLOv5 and YOLOv8 models using weighted box fusion. Precisely measuring the model performance is key to understanding how well your ensemble method is working.

For calculating precision, recall, and mAP (mean Average Precision) at IoU threshold 0.5 ([email protected]) after an ensemble operation like weighted box fusion, you can leverage the val.py script provided in our repo. Here's how you can do it:

1. Ensure your dataset is in a supported format (e.g., COCO).
2. Use your ensemble predictions (in the correct format) as the input to val.py.

A simple command to do this would look something like:

python val.py --weights yolov5_model.pt yolov8_model.pt --data your_dataset.yaml --iou-thres 0.5

Regarding the difference between precision and recall calculations in object detection vs. classification: In classification, each prediction is simply right or wrong, making precision and recall straightforward to compute. In object detection, however, precision and recall are calculated based on the Intersection Over Union (IoU) between predicted bounding boxes and ground truth, considering both the location and the class of the objects. For an object to be considered correctly detected (True Positive), its predicted bounding box needs to have an IoU above a certain threshold with a ground truth box, and the class must match.

Remember, this is a simplified explanation; the actual implementation considers multiple factors like different IoU thresholds and handling multiple detections of the same object.

For more detailed information and guidelines, please refer to our documentation at https://docs.ultralytics.com/yolov5/. Keep pushing the boundaries, and happy modeling! 🚀

from yolov5.

sir i don't think so that we can make ensemble of yolov5 and yolov8 by using val.py file

from yolov5.

@KAKAROT12419 hello! 😊 You're right, and I appreciate your attention to details. My earlier response was a bit misleading on that part. For ensembling YOLOv5 and YOLOv8 models, you'd typically perform model predictions separately and then apply an ensemble method like Weighted Box Fusion on the prediction outputs.

Here’s a brief example of how you might approach it:

1. Generate predictions from each model.
2. Apply Weighted Box Fusion or any ensemble method on these predictions.

The ensembling process itself would happen post-prediction and isn't a direct feature of the val.py script. My apologies for any confusion, and thank you for bringing this up! Keep experimenting and sharing your insights. 🚀

from yolov5.

Can you provide me with code of precision,recall and map50.

from yolov5.

Hello! 😊 For calculating precision, recall, and [email protected] with YOLOv5, you don't need separate code. These metrics are automatically computed during validation if you use the val.py script on your dataset.

Here's how you can do it briefly:

python val.py --weights your_trained_model.pt --data your_dataset.yaml

This command will evaluate your model on the specified dataset and output the precision, recall, and [email protected] among other metrics. Make sure your dataset is properly formatted and your_dataset.yaml points to the right paths.

Happy coding! 🚀

from yolov5.

okay sir thankyou

from yolov5.

You're welcome! 😊 If you have any more questions or need further assistance, feel free to ask. Happy coding! 🚀

from yolov5.