eval mAP implemented in the repository lower than that of pycocotools?

anonymoussss commented 3 years ago

❔Question

I am using yolov5m to train my custom dataset. When using val.py for validation, why is the evaluation map implemented in this repository lower than that of pycocotools? The specific output is as follows.

Additional context

Scanning 'val2017/labels.cache' images and labels.. Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|█| 48/48 [00:19<00:00 all 1530 2011 0.948 0.924 0.929 0.775
Speed: 0.1ms pre-process, 2.5ms inference, 0.5ms NMS per image at shape (32, 3, 320, 320)

Evaluating pycocotools mAP... saving runs/test/exp18/best_predictions.json...
loading annotations into memory...
Done (t=0.07s)
creating index...
index created!
Loading and preparing results...
DONE (t=0.01s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type bbox
DONE (t=0.41s).
Accumulating evaluation results...
DONE (t=0.13s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.760
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.910 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.817
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.512
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.643
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.831
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.638
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.791
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.794
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.555
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.705
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.858
Results saved to runs/test/exp18
1513 labels saved to runs/test/exp18/labels .

github-actions[bot] commented 3 years ago

👋 Hello @anonymoussss, 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.

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glenn-jocher commented 3 years ago

@anonymoussss see https://github.com/ultralytics/yolov5/issues/2258

ultralytics / yolov5