Open ikmal-asraf opened 4 weeks ago
Not an expert at all but how did you train your model ? I think (if you follow most tutorials), there is several data augmentations step performed by default (pretty hidden) on the training dataset. That was messing up my training a lot, especially the mixup one.
@YGBRS , I use Roboflow to store and data augmentations , to train I use Google Colab notebook from here https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolo-nas-on-custom-dataset.ipynb#scrollTo=SdQ5JGblbJTk .
You can see the list of default data transforms done on your training data with train_data.dataset.transforms
If you want to remove some transforms that may or may not mess up your training you can remove them with train_data.dataset.transforms.pop(index)
.
You can plot a sample of your training data with the transforms with train_data.dataset.plot()
If you see the Mixup one , try to remove it and retrain your model. Just test whatever you want from there.
It depends if you see the COCO dataset the average of the images have a pixel rate, if you are training to train imagen that the size are too different the results could change, so maybe if you resized your dataset would help
💡 Your Question
I was doing a object detection project , which is a drowning detection . Below is the result of YOLO-NAS S with 250 epochs.
Despite having high recall and mAP value , both precision and f1 score is low under (0.1) . When testing the model , it do quite well on detecting class despite having low precision .
So I try to find if other people face the same issue . 1.https://github.com/Deci-AI/super-gradients/issues/1191 2.https://github.com/Deci-AI/super-gradients/issues/1734 3.Performance Analysis of YOLO-NAS SOTA Models on CAL Tool Detection YoloNASCALPaper-3.pdf![image](https://github.com/Deci-AI/super-gradients/assets/117511292/a94300b9-6594-470f-bde0-5b5bcc031ece)
4.ASSESSING THE EFFECTIVENESS OF YOLO ARCHITECTURES FOR SMOKE AND WILDFIRE DETECTION Assessing_the_Effectiveness_of_YOLO_Architectures_for_Smoke_and_Wildfire_Detection.pdf
![image](https://github.com/Deci-AI/super-gradients/assets/117511292/042512f7-0266-4752-ab45-c9e9374ede6d)
5.COMPARATIVE STUDY OF YOLOV8 AND YOLO-NAS FOR AGRICULTURE APPLICATION Comparative_study_of_YOLOv8_and_YOLO-NAS_for_agriculture_application.pdf![image](https://github.com/Deci-AI/super-gradients/assets/117511292/6f86eaae-19fa-473f-9040-88d108686503)
From the 5 cases above , it seem like having very low precision but at the same time high recall and mAP is quite normal for YOLO-NAS. When compare with other YOLO version with same dataset, the other YOLO model does not face the same problem . Why does this happen , considering that YOLO NAS generally perform better than other lower YOLO model? Is it because how different YOLO-NAS been evaluate ?
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