mlcommons / mlcommons/inference

Issue with benchmarking SSD-ResNet34 model.

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Description

I'm not able to get proper mAP for ssd-resnet34 model, could someone help with the script. im using pycocotools to evaluate.

DONE (t=1.28s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001

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First steps

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  3. Fork the repository and make your change on a branch.
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Research direction

Start by locating the SSD-ResNet34 benchmarking script and reviewing how its outputs are passed to pycocotools. Reproduce the reported evaluation and compare the generated predictions with the expected COCO evaluation inputs; done means explaining the zero mAP or identifying the configuration or data issue. No file or test is named in the issue.

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Assessment

Tech stack
python
Domain
machine-learning
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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