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Video Inference, Transfer Learning Improvements

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@glenn-jocher glenn-jocher released this 03 Apr 10:38
· 2205 commits to master since this release

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

  • None

Added Functionality

  • Video Inference. detect.py now automatically processes both images and videos. Image and video results are saved in their respective formats now (video inference saves new videos).
  • Transfer learning now operates automatically regardless of yolo layer size #152.

Performance

https://cloud.google.com/deep-learning-vm/
Machine type: n1-standard-8 (8 vCPUs, 30 GB memory)
CPU platform: Intel Skylake
GPUs: K80 ($0.198/hr), P4 ($0.279/hr), T4 ($0.353/hr), P100 ($0.493/hr), V100 ($0.803/hr)
HDD: 100 GB SSD
Dataset: COCO train 2014

GPUs batch_size batch time epoch time epoch cost
(images) (s/batch)
1 K80 16 1.43s 175min $0.58
1 P4 8 0.51s 125min $0.58
1 T4 16 0.78s 94min $0.55
1 P100 16 0.39s 48min $0.39
2 P100 32 0.48s 29min $0.47
4 P100 64 0.65s 20min $0.65
1 V100 16 0.25s 31min $0.41
2 V100 32 0.29s 18min $0.48
4 V100 64 0.41s 13min $0.70
8 V100 128 0.49s 7min $0.80

TODO (help and PR's welcome!)

  • Add iOS App inference to photos and videos in Camera Roll.
  • Add parameter to switch between 'darknet' and 'power' wh methods. #168
  • YAPF linting (including possible wrap to PEP8 79 character-line standard) #88.
  • Hyperparameter search for loss function constants.