abeardear/pytorch-YOLO-v1
A PyTorch implementation of YOLO-v1 object detection using ResNet50 backbone.
Not currently ranked — collecting fresh signals.
star history
This repository contains an experimental implementation of the YOLO-v1 object detection algorithm in PyTorch. The author replaced the original darknet backbone with ResNet50 to leverage pretrained weights. The code supports training on VOC2007+2012 datasets and evaluation, achieving 66.5% mAP at 57 FPS on a GTX 1080 Ti. It includes loss computation using autograd, data preparation scripts, and inference visualization.
Frequently asked
- What is abeardear/pytorch-YOLO-v1?
- A PyTorch implementation of YOLO-v1 object detection using ResNet50 backbone.
- Is pytorch-YOLO-v1 open source?
- Yes — abeardear/pytorch-YOLO-v1 is open source, released under the MIT license.
- What language is pytorch-YOLO-v1 written in?
- abeardear/pytorch-YOLO-v1 is primarily written in Python.
- How popular is pytorch-YOLO-v1?
- abeardear/pytorch-YOLO-v1 has 675 stars on GitHub.
- Where can I find pytorch-YOLO-v1?
- abeardear/pytorch-YOLO-v1 is on GitHub at https://github.com/abeardear/pytorch-YOLO-v1.