Multi-GPU, Tutorials, Official COCO mAP Support

This release requires PyTorch >= v1.0.0 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

• Multi GPU support #21.

Added Functionality

• Tutorial created: https://github.com/ultralytics/yolov3/wiki/Example:-Transfer-Learning
• Tutorial created: https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Image
• Tutorial created: https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class
• Tutorial created: https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data
• test.py optionally outputs pycocotools compatible json files now with the --save-json flag, and computes official COCO mAP using pycocotools. Output is verified against official darknet results from https://arxiv.org/abs/1804.02767. #2 (comment).

	ultralytics/yolov3 mAP	darknet mAP
YOLOv3-320	51.3	51.5
YOLOv3-416	54.9	55.3
YOLOv3-608	57.9	57.9

Performance

• 10% improvement in training speed via code optimization. Performance should be further improved by multithreading the dataloader (on the TODO list).

https://cloud.google.com/deep-learning-vm/
Machine type: n1-highmem-4 (4 vCPUs, 26 GB memory)
CPU platform: Intel Skylake
GPUs: 1-4 x NVIDIA Tesla P100
HDD: 100 GB SSD

GPUs	batch_size	speed	COCO epoch
(P100)	(images)	(s/batch)	(min/epoch)
1	16	0.54s	66min
2	32	0.99s	61min
4	64	1.61s	49min

TODO (help and PR's welcome!)

• Dataloader should enable multithread. Single thread loading takes about 230 ms per batch currently, out of total batch time of 550 ms (40%). Low GPU utilization reported. #141
• test.py should natively output the same results as pycocotools #2
• Video Inference. Pass a video file to detect.py.
• YAPF linting (including possible wrap to PEP8 79 character-line standard) #88.
• Add iOS App inference to photos and videos in Camera Roll.
• Add parameter to switch between 'darknet' and 'power' wh methods.