Why the results of the detect script are not the same as the results of the val script？

[ThreeStones1029]

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Question

Why the results of the two scripts detect and val are not the same, and much worse. The reproduction process:

1. I used the same model and validation set.
2. I set the same confidence and iou and the maximum number of detections.
3. I ran the val script and saved the json result and converted it to a format suitable for coco evaluation.
4. I save the results of the detect script as a separate json file. But when I used coco's api to evaluate these two results, the map was far apart? The first one is the result of the val script evaluated using coco after it is run.

   Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.323
   Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.519
   Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.363
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.035
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.344
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.251
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.444
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.444
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.069
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.480

   The second is the result evaluated using coco after the detect script is run.

   Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.230
   Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.427
   Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.247
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.039
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.289
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.107
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.281
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.281
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.062
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.322

   I also found that there are more anns in the json file produced by the val script than in the json file produced by the detect script. ann is a dictionary like this.

   {
       "image_id": 63,
       "category_id": 4,
       "bbox": [
           389.792,
           537.801,
           336.013,
           207.175
       ],
       "score": 0.96367,
       "file_name": "ceng_fu_di_5.bmp",
       "category_name": "L2"
   }

   Hope you can get a reply, thank you very much!

Additional

No response

[github-actions[bot]]

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[glenn-jocher]

@ThreeStones1029 hello,

Thank you for your detailed report and for providing the reproduction steps. To help us investigate the issue further, could you please provide a minimum reproducible code example? This will allow us to better understand the context and replicate the issue on our end. You can refer to our guide on creating a minimum reproducible example here: Minimum Reproducible Example.

Additionally, please ensure that you are using the latest versions of torch and the YOLOv5 repository. You can update your packages with the following commands:

pip install --upgrade torch
git pull https://github.com/ultralytics/yolov5

There are a few potential reasons for the discrepancy between the detect.py and val.py results:

1. Post-processing Differences: The detect.py script might apply different post-processing steps compared to val.py. Ensure that the confidence threshold, IoU threshold, and other parameters are consistent between the two scripts.

2. Evaluation Metrics: The val.py script is specifically designed for evaluation and might include additional metrics or processing steps that are not present in detect.py.

3. Annotation Differences: As you mentioned, there are more annotations in the JSON file produced by val.py. This could be due to differences in how detections are filtered or processed in each script.

To further diagnose the issue, you can try the following steps:

1. Consistency Check: Ensure that both scripts are using the same model weights, dataset, and configuration parameters.

2. Debugging: Add print statements or logging to both scripts to compare the intermediate outputs and identify where the differences arise.

3. Manual Inspection: Manually inspect a few samples from the JSON files produced by both scripts to understand the nature of the discrepancies.

If you can share the specific code snippets or configurations you are using for both scripts, it would be greatly helpful. We appreciate your patience and cooperation as we work together to resolve this issue.

[github-actions[bot]]

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