A minimal PyTorch implementation of YOLOv3, with support for inference. This is a wrapper of YOLOV3-pytorch implementation here as a standalone python package.
$ pip install git+https://github.com/mkocabas/yolov3-pytorch.git
| 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 |
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 |
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)- 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