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ultralytics/yolov3: 43.1mAP@0.5:0.95 on COCO2014

Authors: Glenn Jocher; Yonghye Kwon; guigarfr; Josh Veitch-Michaelis; perry0418; Ttayu; Marc; +18 Authors

ultralytics/yolov3: 43.1mAP@0.5:0.95 on COCO2014

Abstract

This release requires PyTorch >= v1.4 to function properly. Please install the latest version from https://github.com/pytorch/pytorch/releases Breaking Changes There are no breaking changes in this release. Bug Fixes Various Added Functionality Improved training and test ground truth and prediction plotting. https://github.com/ultralytics/yolov3/pull/1114 Increased augmentation speed. https://github.com/ultralytics/yolov3/pull/1110 Improved Tensorboard integration. Auto class hyperparameter update based on dataset class count. Inference time augmentation option added now with --augment argument in test.py and detect.py. Rectangular training with --rect argument in train.py Speed https://cloud.google.com/deep-learning-vm/ Machine type: preemptible n1-standard-8 (8 vCPUs, 30 GB memory) CPU platform: Intel Skylake GPUs: K80 ($0.14/hr), T4 ($0.11/hr), V100 ($0.74/hr) CUDA with Nvidia Apex FP16/32 HDD: 300 GB SSD Dataset: COCO train 2014 (117,263 images) Model: yolov3-spp.cfg Command: python3 train.py --data coco2017.data --img 416 --batch 32 GPU n --batch-size img/s epoch<br>time epoch<br>cost K80 1 32 x 2 11 175 min $0.41 T4 1<br>2 32 x 2<br>64 x 1 41<br>61 48 min<br>32 min $0.09<br>$0.11 V100 1<br>2 32 x 2<br>64 x 1 122<br>178 16 min<br>11 min $0.21<br>$0.28 2080Ti 1<br>2 32 x 2<br>64 x 1 81<br>140 24 min<br>14 min -<br>- mAP <i></i> Size COCO mAP<br>@0.5...0.95 COCO mAP<br>@0.5 YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>YOLOv3-SPP-ultralytics 320 14.0<br>28.7<br>30.5<br>37.7 29.1<br>51.8<br>52.3<br>56.8 YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>YOLOv3-SPP-ultralytics 416 16.0<br>31.2<br>33.9<br>41.2 33.0<br>55.4<br>56.9<br>60.6 YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>YOLOv3-SPP-ultralytics 512 16.6<br>32.7<br>35.6<br>42.6 34.9<br>57.7<br>59.5<br>62.4 YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>YOLOv3-SPP-ultralytics 608 16.6<br>33.1<br>37.0<br>43.1 35.4<br>58.2<br>60.7<br>62.8 TODO (help and PR's welcome!) Add iOS App inference to photos and videos in Camera Roll, as well as 'Flexible', or at least rectangular inference. https://github.com/ultralytics/yolov3/issues/224

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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