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

This release requires PyTorch &gt;= 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 <code>--augment</code> argument in test.py and detect.py. Rectangular training with <code>--rect</code> argument in train.py Speed https://cloud.google.com/deep-learning-vm/<br> <strong>Machine type:</strong> preemptible n1-standard-8 (8 vCPUs, 30 GB memory)<br> <strong>CPU platform:</strong> Intel Skylake<br> <strong>GPUs:</strong> K80 ($0.14/hr), T4 ($0.11/hr), V100 ($0.74/hr) CUDA with Nvidia Apex FP16/32<br> <strong>HDD:</strong> 300 GB SSD <strong>Dataset:</strong> COCO train 2014 (117,263 images)<br> <strong>Model:</strong> <code>yolov3-spp.cfg</code><br> <strong>Command:</strong> <code>python3 train.py --data coco2017.data --img 416 --batch 32</code> GPU n <code>--batch-size</code> img/s epoch&lt;br&gt;time epoch&lt;br&gt;cost K80 1 32 x 2 11 175 min $0.41 T4 1&lt;br&gt;2 32 x 2&lt;br&gt;64 x 1 41&lt;br&gt;61 48 min&lt;br&gt;32 min $0.09&lt;br&gt;$0.11 V100 1&lt;br&gt;2 32 x 2&lt;br&gt;64 x 1 122&lt;br&gt;<strong>178</strong> 16 min&lt;br&gt;<strong>11 min</strong> <strong>$0.21</strong>&lt;br&gt;$0.28 2080Ti 1&lt;br&gt;2 32 x 2&lt;br&gt;64 x 1 81&lt;br&gt;140 24 min&lt;br&gt;14 min -&lt;br&gt;- mAP &lt;i&gt;&lt;/i&gt; Size COCO mAP&lt;br&gt;@0.5...0.95 COCO mAP&lt;br&gt;@0.5 YOLOv3-tiny&lt;br&gt;YOLOv3&lt;br&gt;YOLOv3-SPP&lt;br&gt;<strong>YOLOv3-SPP-ultralytics</strong> 320 14.0&lt;br&gt;28.7&lt;br&gt;30.5&lt;br&gt;<strong>37.7</strong> 29.1&lt;br&gt;51.8&lt;br&gt;52.3&lt;br&gt;<strong>56.8</strong> YOLOv3-tiny&lt;br&gt;YOLOv3&lt;br&gt;YOLOv3-SPP&lt;br&gt;<strong>YOLOv3-SPP-ultralytics</strong> 416 16.0&lt;br&gt;31.2&lt;br&gt;33.9&lt;br&gt;<strong>41.2</strong> 33.0&lt;br&gt;55.4&lt;br&gt;56.9&lt;br&gt;<strong>60.6</strong> YOLOv3-tiny&lt;br&gt;YOLOv3&lt;br&gt;YOLOv3-SPP&lt;br&gt;<strong>YOLOv3-SPP-ultralytics</strong> 512 16.6&lt;br&gt;32.7&lt;br&gt;35.6&lt;br&gt;<strong>42.6</strong> 34.9&lt;br&gt;57.7&lt;br&gt;59.5&lt;br&gt;<strong>62.4</strong> YOLOv3-tiny&lt;br&gt;YOLOv3&lt;br&gt;YOLOv3-SPP&lt;br&gt;<strong>YOLOv3-SPP-ultralytics</strong> 608 16.6&lt;br&gt;33.1&lt;br&gt;37.0&lt;br&gt;<strong>43.1</strong> 35.4&lt;br&gt;58.2&lt;br&gt;60.7&lt;br&gt;<strong>62.8</strong> 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