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 is now working correctly https://github.com/ultralytics/yolov3/issues/21.
• test.py now natively outputs the same results as pycocotools to within 1% under most circumstances https://github.com/ultralytics/yolov3/issues/2

Added Functionality

• Dataloader is now multithread. https://github.com/ultralytics/yolov3/issues/141
• mAP improved by smarter NMS. mAP now exceeds darknet mAP by a small amount in all image sizes 320-608.

sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
# bash yolov3/data/get_coco_dataset.sh
sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
cd yolov3

python3 test.py --save-json --conf-thres 0.001 --img-size 416
Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights')
Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
      Image      Total          P          R        mAP
Calculating mAP: 100%|█████████████████████████████████| 157/157 [08:34<00:00,  2.53s/it]
       5000       5000     0.0896      0.756      0.555
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.312
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.554
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.317
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.343
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.452
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.268
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.411
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.435
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.244
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.477
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.587
 
python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16
Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights')
Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
      Image      Total          P          R        mAP
Calculating mAP: 100%|█████████████████████████████████| 313/313 [08:54<00:00,  1.55s/it]
       5000       5000     0.0966      0.786      0.579
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.331
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.582
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.344
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.198
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.362
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.427
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.281
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.437
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.463
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.309
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577

Performance

• mAP computation is much slower now than before using default settings, as --conf-thres 0.001 captures many boxes that all must be passed through NMS. On a V100 test.py runs in about 8 minutes
• Training speed is improved substantially compared to v3.0 due to the addition of the multithreaded PyTorch dataloader.

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

TODO (help and PR's welcome!)

• Video Inference. Pass a video file to detect.py.
• YAPF linting (including possible wrap to PEP8 79 character-line standard) https://github.com/ultralytics/yolov3/issues/88.
• Add iOS App inference to photos and videos in Camera Roll.
• Add parameter to switch between 'darknet' and 'power' wh methods. https://github.com/ultralytics/yolov3/issues/168
• Hyperparameter search for loss function constants.