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* Improve mAP0.5-0.95
Two changes provided
1. Added limit on the maximum number of detections for each image likewise pycocotools
2. Rework process_batch function
Changes #2 solved issue #4251
I also independently encountered the problem described in issue #4251 that the values for the same thresholds do not match when changing the limits in the torch.linspace function.
These changes solve this problem.
Currently during validation yolov5x.pt model the following results were obtained:
from yolov5 validation
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 157/157 [01:07<00:00, 2.33it/s]
all 5000 36335 0.743 0.626 0.682 0.506
from pycocotools
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.505
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.685
These results are very close, although not completely pass the competition issue #2258.
I think it's problem with false positive bboxes matched ignored criteria, but this is not actual for custom datasets and does not require an additional solution.
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* Remove line to retain pycocotools results
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* Removed shell=True from subprocess commands that require user inputs. Also removed unused arguments
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* Added check=True
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* Uses OpenVINO runtime instead of openvino-dev
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* New TensorFlow `TFCrossConv()` module
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* Implement DDP `static_graph=True`
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* Pin downloads to release version
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* Ability to dowlnoad older assets
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* Update tutorial.ipynb
* Created using Colaboratory
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* FROM nvcr.io/nvidia/pytorch:22.04-py3
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