YoloV3

A minimal PyTorch implementation of YOLOv3, with support for inference. This is a wrapper of YOLOV3-pytorch implementation here as a standalone python package.

Install

$ pip install git+https://github.com/mkocabas/yolov3-pytorch.git

Performance

Model	mAP (min. 50 IoU)
YOLOv3 608 (paper)	57.9
YOLOv3 608 (this impl.)	57.3
YOLOv3 416 (paper)	55.3
YOLOv3 416 (this impl.)	55.5

Inference

Uses pretrained weights to make predictions on images.

$ python examples/detect.py --image_folder data/samples/

Below table displays the inference times when using as inputs images scaled to 256x256.

Backbone	GPU	FPS
ResNet-101	Titan X	53
ResNet-152	Titan X	37
Darknet-53 (paper)	Titan X	76
Darknet-53 (this impl.)	1080ti	74

Sample usage as a package

from yolov3.yolo import YOLOv3
from torchvision.transforms import Compose, ToTensor, Normalize

detector = YOLOv3(device='cuda', img_size=608)
preprocess_img = Compose([
        ToTensor(),
        Normalize(mean=[0.485, 0.456, 0.406],
                  std=[0.229, 0.224, 0.225])
])

img = preprocess_img('data/sample.png').to('cuda')

predictions = detector(img)

Credit

• YOLOv3: An Incremental Improvement

Joseph Redmon, Ali Farhadi

[Paper] [Project Webpage] [Authors' Implementation]

@article{yolov3,
  title={YOLOv3: An Incremental Improvement},
  author={Redmon, Joseph and Farhadi, Ali},
  journal = {arXiv},
  year={2018}
}

• YOLOv3 PyTorch implementation: link